From 8113cfb2573ec64be08fef22d6682e81c0416bd1 Mon Sep 17 00:00:00 2001 From: Madeesh Kannan Date: Wed, 5 Jul 2023 13:36:04 +0200 Subject: [PATCH 01/74] `Language.replace_listeners`: Pass the replaced listener and the `tok2vec` pipe to the callback (#12785) * `Language.replace_listeners`: Pass the replaced listener and the `tok2vec` pipe to the callback * Update developer docs * `isort` fixes * Add error message to assertion * Add clarification to dev docs * Replace assertion with exception * Doc fixes --- extra/DEVELOPER_DOCS/Listeners.md | 39 +++++++++++++++++++++---------- spacy/errors.py | 2 ++ spacy/language.py | 17 ++++++++++++-- 3 files changed, 44 insertions(+), 14 deletions(-) diff --git a/extra/DEVELOPER_DOCS/Listeners.md b/extra/DEVELOPER_DOCS/Listeners.md index 3a71082e0..72c036880 100644 --- a/extra/DEVELOPER_DOCS/Listeners.md +++ b/extra/DEVELOPER_DOCS/Listeners.md @@ -1,14 +1,17 @@ # Listeners -1. [Overview](#1-overview) -2. [Initialization](#2-initialization) - - [A. Linking listeners to the embedding component](#2a-linking-listeners-to-the-embedding-component) - - [B. Shape inference](#2b-shape-inference) -3. [Internal communication](#3-internal-communication) - - [A. During prediction](#3a-during-prediction) - - [B. During training](#3b-during-training) - - [C. Frozen components](#3c-frozen-components) -4. [Replacing listener with standalone](#4-replacing-listener-with-standalone) +- [1. Overview](#1-overview) +- [2. Initialization](#2-initialization) + - [2A. Linking listeners to the embedding component](#2a-linking-listeners-to-the-embedding-component) + - [2B. Shape inference](#2b-shape-inference) +- [3. Internal communication](#3-internal-communication) + - [3A. During prediction](#3a-during-prediction) + - [3B. During training](#3b-during-training) + - [Training with multiple listeners](#training-with-multiple-listeners) + - [3C. Frozen components](#3c-frozen-components) + - [The Tok2Vec or Transformer is frozen](#the-tok2vec-or-transformer-is-frozen) + - [The upstream component is frozen](#the-upstream-component-is-frozen) +- [4. Replacing listener with standalone](#4-replacing-listener-with-standalone) ## 1. Overview @@ -62,7 +65,7 @@ of this `find_listener()` method will specifically identify sublayers of a model If it's a Transformer-based pipeline, a [`transformer` component](https://github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py) -has a similar implementation but its `find_listener()` function will specifically look for `TransformerListener` +has a similar implementation but its `find_listener()` function will specifically look for `TransformerListener` sublayers of downstream components. ### 2B. Shape inference @@ -154,7 +157,7 @@ as a tagger or a parser. This used to be impossible before 3.1, but has become s embedding component in the [`annotating_components`](https://spacy.io/usage/training#annotating-components) list of the config. This works like any other "annotating component" because it relies on the `Doc` attributes. -However, if the `Tok2Vec` or `Transformer` is frozen, and not present in `annotating_components`, and a related +However, if the `Tok2Vec` or `Transformer` is frozen, and not present in `annotating_components`, and a related listener isn't frozen, then a `W086` warning is shown and further training of the pipeline will likely end with `E954`. #### The upstream component is frozen @@ -216,5 +219,17 @@ new_model = tok2vec_model.attrs["replace_listener"](new_model) ``` The new config and model are then properly stored on the `nlp` object. -Note that this functionality (running the replacement for a transformer listener) was broken prior to +Note that this functionality (running the replacement for a transformer listener) was broken prior to `spacy-transformers` 1.0.5. + +In spaCy 3.7, `Language.replace_listeners` was updated to pass the following additional arguments to the `replace_listener` callback: +the listener to be replaced and the `tok2vec`/`transformer` pipe from which the new model was copied. To maintain backwards-compatiblity, +the method only passes these extra arguments for callbacks that support them: + +``` +def replace_listener_pre_37(copied_tok2vec_model): + ... + +def replace_listener_post_37(copied_tok2vec_model, replaced_listener, tok2vec_pipe): + ... +``` diff --git a/spacy/errors.py b/spacy/errors.py index db1a886aa..a2f8ca85c 100644 --- a/spacy/errors.py +++ b/spacy/errors.py @@ -981,6 +981,8 @@ class Errors(metaclass=ErrorsWithCodes): " 'min_length': {min_length}, 'max_length': {max_length}") E1054 = ("The text, including whitespace, must match between reference and " "predicted docs when training {component}.") + E1055 = ("The 'replace_listener' callback expects {num_params} parameters, " + "but only callbacks with one or three parameters are supported") # Deprecated model shortcuts, only used in errors and warnings diff --git a/spacy/language.py b/spacy/language.py index fd616483b..6a848bf9a 100644 --- a/spacy/language.py +++ b/spacy/language.py @@ -1,4 +1,5 @@ import functools +import inspect import itertools import multiprocessing as mp import random @@ -2033,8 +2034,20 @@ class Language: # Go over the listener layers and replace them for listener in pipe_listeners: new_model = tok2vec_model.copy() - if "replace_listener" in tok2vec_model.attrs: - new_model = tok2vec_model.attrs["replace_listener"](new_model) + replace_listener_func = tok2vec_model.attrs.get("replace_listener") + if replace_listener_func is not None: + # Pass the extra args to the callback without breaking compatibility with + # old library versions that only expect a single parameter. + num_params = len( + inspect.signature(replace_listener_func).parameters + ) + if num_params == 1: + new_model = replace_listener_func(new_model) + elif num_params == 3: + new_model = replace_listener_func(new_model, listener, tok2vec) + else: + raise ValueError(Errors.E1055.format(num_params=num_params)) + util.replace_model_node(pipe.model, listener, new_model) # type: ignore[attr-defined] tok2vec.remove_listener(listener, pipe_name) From d195923164823d8ce207506862b35c60463188ea Mon Sep 17 00:00:00 2001 From: Madeesh Kannan Date: Thu, 6 Jul 2023 18:29:03 +0200 Subject: [PATCH 02/74] Set version to `3.7.0.dev0` (#12799) --- spacy/about.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spacy/about.py b/spacy/about.py index cad6158da..71a728128 100644 --- a/spacy/about.py +++ b/spacy/about.py @@ -1,6 +1,6 @@ # fmt: off __title__ = "spacy" -__version__ = "3.6.0" +__version__ = "3.7.0.dev0" __download_url__ = "https://github.com/explosion/spacy-models/releases/download" __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json" __projects__ = "https://github.com/explosion/projects" From 991bcc111e1a35cc96dba32ac08c212b0b360384 Mon Sep 17 00:00:00 2001 From: svlandeg Date: Fri, 7 Jul 2023 08:09:57 +0200 Subject: [PATCH 03/74] disable tests until 3.7 models are available --- .github/workflows/tests.yml | 54 ++++++++++++++++++------------------- spacy/tests/test_cli.py | 2 ++ 2 files changed, 29 insertions(+), 27 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d60c90c1c..f177fbcb6 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -111,22 +111,22 @@ jobs: - name: Test import run: python -W error -c "import spacy" - - name: "Test download CLI" - run: | - python -m spacy download ca_core_news_sm - python -m spacy download ca_core_news_md - python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')" - if: matrix.python_version == '3.9' - - - name: "Test download_url in info CLI" - run: | - python -W error -m spacy info ca_core_news_sm | grep -q download_url - if: matrix.python_version == '3.9' - - - name: "Test no warnings on load (#11713)" - run: | - python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')" - if: matrix.python_version == '3.9' +# - name: "Test download CLI" +# run: | +# python -m spacy download ca_core_news_sm +# python -m spacy download ca_core_news_md +# python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')" +# if: matrix.python_version == '3.9' +# +# - name: "Test download_url in info CLI" +# run: | +# python -W error -m spacy info ca_core_news_sm | grep -q download_url +# if: matrix.python_version == '3.9' +# +# - name: "Test no warnings on load (#11713)" +# run: | +# python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')" +# if: matrix.python_version == '3.9' - name: "Test convert CLI" run: | @@ -150,17 +150,17 @@ jobs: python -m spacy train ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy --training.max_steps 10 --gpu-id -1 if: matrix.python_version == '3.9' - - name: "Test assemble CLI" - run: | - python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')" - PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir - if: matrix.python_version == '3.9' - - - name: "Test assemble CLI vectors warning" - run: | - python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')" - python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113 - if: matrix.python_version == '3.9' +# - name: "Test assemble CLI" +# run: | +# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')" +# PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir +# if: matrix.python_version == '3.9' +# +# - name: "Test assemble CLI vectors warning" +# run: | +# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')" +# python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113 +# if: matrix.python_version == '3.9' - name: "Install test requirements" run: | diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py index 8e1c9ca32..f5a7aadb8 100644 --- a/spacy/tests/test_cli.py +++ b/spacy/tests/test_cli.py @@ -697,6 +697,7 @@ def test_string_to_list_intify(value): assert string_to_list(value, intify=True) == [1, 2, 3] +@pytest.mark.skip(reason="Temporarily skip before 3.7 models are published") def test_download_compatibility(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False @@ -707,6 +708,7 @@ def test_download_compatibility(): assert get_minor_version(about.__version__) == get_minor_version(version) +@pytest.mark.skip(reason="Temporarily skip before 3.7 models are published") def test_validate_compatibility_table(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False From b1b20bf69df6113b51fd94b5249188d9b9e4c1b4 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Fri, 7 Jul 2023 09:10:27 +0200 Subject: [PATCH 04/74] Replace projects functionality with weasel (#12769) * Setting up weasel branch (#12456) * remove project-specific functionality * remove project-specific tests * remove project-specific schemas * remove project-specific information in about * remove project-specific functions in util.py * remove project-specific error strings * remove project-specific CLI commands * black formatting * restore some functions that are used beyond projects * remove project imports * remove imports * remove remote_storage tests * remove one more project unit test * update for PR 12394 * remove get_hash and get_checksum * remove upload_ and download_file methods * remove ensure_pathy * revert clumsy fingers * reinstate E970 * feat: use weasel as spacy project command (#12473) * feat: use weasel as spacy project command * build: use constrained requirement for weasel * feat: add weasel to the library requirements * build: update weasel to new version * build: use specific weasel tag * build: use weasel-0.1.0rc1 from PyPI * fix: remove weasel from requirements.txt * fix: requirements.txt and setup.cfg need to reflect each other * feat: remove legacy spacy project code * bump version * further merge fixes * isort --------- Co-authored-by: Basile Dura --- requirements.txt | 1 + setup.cfg | 1 + spacy/about.py | 2 - spacy/cli/__init__.py | 7 - spacy/cli/_util.py | 329 +----------------------- spacy/cli/project/__init__.py | 0 spacy/cli/project/assets.py | 217 ---------------- spacy/cli/project/clone.py | 124 --------- spacy/cli/project/document.py | 115 --------- spacy/cli/project/dvc.py | 220 ---------------- spacy/cli/project/pull.py | 67 ----- spacy/cli/project/push.py | 69 ----- spacy/cli/project/remote_storage.py | 212 ---------------- spacy/cli/project/run.py | 379 ---------------------------- spacy/errors.py | 2 - spacy/schemas.py | 60 ----- spacy/tests/test_cli.py | 293 +-------------------- spacy/util.py | 24 -- 18 files changed, 9 insertions(+), 2113 deletions(-) delete mode 100644 spacy/cli/project/__init__.py delete mode 100644 spacy/cli/project/assets.py delete mode 100644 spacy/cli/project/clone.py delete mode 100644 spacy/cli/project/document.py delete mode 100644 spacy/cli/project/dvc.py delete mode 100644 spacy/cli/project/pull.py delete mode 100644 spacy/cli/project/push.py delete mode 100644 spacy/cli/project/remote_storage.py delete mode 100644 spacy/cli/project/run.py diff --git a/requirements.txt b/requirements.txt index a007f495e..f5050fee2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,7 @@ catalogue>=2.0.6,<2.1.0 typer>=0.3.0,<0.10.0 pathy>=0.10.0 smart-open>=5.2.1,<7.0.0 +weasel>=0.1.0,<0.2.0 # Third party dependencies numpy>=1.15.0 requests>=2.13.0,<3.0.0 diff --git a/setup.cfg b/setup.cfg index 45734888f..048bb3719 100644 --- a/setup.cfg +++ b/setup.cfg @@ -51,6 +51,7 @@ install_requires = wasabi>=0.9.1,<1.2.0 srsly>=2.4.3,<3.0.0 catalogue>=2.0.6,<2.1.0 + weasel>=0.1.0,<0.2.0 # Third-party dependencies typer>=0.3.0,<0.10.0 pathy>=0.10.0 diff --git a/spacy/about.py b/spacy/about.py index 71a728128..d816926fd 100644 --- a/spacy/about.py +++ b/spacy/about.py @@ -3,5 +3,3 @@ __title__ = "spacy" __version__ = "3.7.0.dev0" __download_url__ = "https://github.com/explosion/spacy-models/releases/download" __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json" -__projects__ = "https://github.com/explosion/projects" -__projects_branch__ = "v3" diff --git a/spacy/cli/__init__.py b/spacy/cli/__init__.py index 549a27616..4fc076f9a 100644 --- a/spacy/cli/__init__.py +++ b/spacy/cli/__init__.py @@ -21,13 +21,6 @@ from .init_pipeline import init_pipeline_cli # noqa: F401 from .package import package # noqa: F401 from .pretrain import pretrain # noqa: F401 from .profile import profile # noqa: F401 -from .project.assets import project_assets # noqa: F401 -from .project.clone import project_clone # noqa: F401 -from .project.document import project_document # noqa: F401 -from .project.dvc import project_update_dvc # noqa: F401 -from .project.pull import project_pull # noqa: F401 -from .project.push import project_push # noqa: F401 -from .project.run import project_run # noqa: F401 from .train import train_cli # noqa: F401 from .validate import validate # noqa: F401 diff --git a/spacy/cli/_util.py b/spacy/cli/_util.py index eff897316..bc6c53cd9 100644 --- a/spacy/cli/_util.py +++ b/spacy/cli/_util.py @@ -25,10 +25,11 @@ from thinc.api import Config, ConfigValidationError, require_gpu from thinc.util import gpu_is_available from typer.main import get_command from wasabi import Printer, msg +from weasel import app as project_cli from .. import about from ..compat import Literal -from ..schemas import ProjectConfigSchema, validate +from ..schemas import validate from ..util import ( ENV_VARS, SimpleFrozenDict, @@ -48,7 +49,6 @@ SDIST_SUFFIX = ".tar.gz" WHEEL_SUFFIX = "-py3-none-any.whl" PROJECT_FILE = "project.yml" -PROJECT_LOCK = "project.lock" COMMAND = "python -m spacy" NAME = "spacy" HELP = """spaCy Command-line Interface @@ -74,11 +74,10 @@ Opt = typer.Option app = typer.Typer(name=NAME, help=HELP) benchmark_cli = typer.Typer(name="benchmark", help=BENCHMARK_HELP, no_args_is_help=True) -project_cli = typer.Typer(name="project", help=PROJECT_HELP, no_args_is_help=True) debug_cli = typer.Typer(name="debug", help=DEBUG_HELP, no_args_is_help=True) init_cli = typer.Typer(name="init", help=INIT_HELP, no_args_is_help=True) -app.add_typer(project_cli) +app.add_typer(project_cli, name="project", help=PROJECT_HELP, no_args_is_help=True) app.add_typer(debug_cli) app.add_typer(benchmark_cli) app.add_typer(init_cli) @@ -153,148 +152,6 @@ def _parse_override(value: Any) -> Any: return str(value) -def load_project_config( - path: Path, interpolate: bool = True, overrides: Dict[str, Any] = SimpleFrozenDict() -) -> Dict[str, Any]: - """Load the project.yml file from a directory and validate it. Also make - sure that all directories defined in the config exist. - - path (Path): The path to the project directory. - interpolate (bool): Whether to substitute project variables. - overrides (Dict[str, Any]): Optional config overrides. - RETURNS (Dict[str, Any]): The loaded project.yml. - """ - config_path = path / PROJECT_FILE - if not config_path.exists(): - msg.fail(f"Can't find {PROJECT_FILE}", config_path, exits=1) - invalid_err = f"Invalid {PROJECT_FILE}. Double-check that the YAML is correct." - try: - config = srsly.read_yaml(config_path) - except ValueError as e: - msg.fail(invalid_err, e, exits=1) - errors = validate(ProjectConfigSchema, config) - if errors: - msg.fail(invalid_err) - print("\n".join(errors)) - sys.exit(1) - validate_project_version(config) - validate_project_commands(config) - if interpolate: - err = f"{PROJECT_FILE} validation error" - with show_validation_error(title=err, hint_fill=False): - config = substitute_project_variables(config, overrides) - # Make sure directories defined in config exist - for subdir in config.get("directories", []): - dir_path = path / subdir - if not dir_path.exists(): - dir_path.mkdir(parents=True) - return config - - -def substitute_project_variables( - config: Dict[str, Any], - overrides: Dict[str, Any] = SimpleFrozenDict(), - key: str = "vars", - env_key: str = "env", -) -> Dict[str, Any]: - """Interpolate variables in the project file using the config system. - - config (Dict[str, Any]): The project config. - overrides (Dict[str, Any]): Optional config overrides. - key (str): Key containing variables in project config. - env_key (str): Key containing environment variable mapping in project config. - RETURNS (Dict[str, Any]): The interpolated project config. - """ - config.setdefault(key, {}) - config.setdefault(env_key, {}) - # Substitute references to env vars with their values - for config_var, env_var in config[env_key].items(): - config[env_key][config_var] = _parse_override(os.environ.get(env_var, "")) - # Need to put variables in the top scope again so we can have a top-level - # section "project" (otherwise, a list of commands in the top scope wouldn't) - # be allowed by Thinc's config system - cfg = Config({"project": config, key: config[key], env_key: config[env_key]}) - cfg = Config().from_str(cfg.to_str(), overrides=overrides) - interpolated = cfg.interpolate() - return dict(interpolated["project"]) - - -def validate_project_version(config: Dict[str, Any]) -> None: - """If the project defines a compatible spaCy version range, chec that it's - compatible with the current version of spaCy. - - config (Dict[str, Any]): The loaded config. - """ - spacy_version = config.get("spacy_version", None) - if spacy_version and not is_compatible_version(about.__version__, spacy_version): - err = ( - f"The {PROJECT_FILE} specifies a spaCy version range ({spacy_version}) " - f"that's not compatible with the version of spaCy you're running " - f"({about.__version__}). You can edit version requirement in the " - f"{PROJECT_FILE} to load it, but the project may not run as expected." - ) - msg.fail(err, exits=1) - - -def validate_project_commands(config: Dict[str, Any]) -> None: - """Check that project commands and workflows are valid, don't contain - duplicates, don't clash and only refer to commands that exist. - - config (Dict[str, Any]): The loaded config. - """ - command_names = [cmd["name"] for cmd in config.get("commands", [])] - workflows = config.get("workflows", {}) - duplicates = set([cmd for cmd in command_names if command_names.count(cmd) > 1]) - if duplicates: - err = f"Duplicate commands defined in {PROJECT_FILE}: {', '.join(duplicates)}" - msg.fail(err, exits=1) - for workflow_name, workflow_steps in workflows.items(): - if workflow_name in command_names: - err = f"Can't use workflow name '{workflow_name}': name already exists as a command" - msg.fail(err, exits=1) - for step in workflow_steps: - if step not in command_names: - msg.fail( - f"Unknown command specified in workflow '{workflow_name}': {step}", - f"Workflows can only refer to commands defined in the 'commands' " - f"section of the {PROJECT_FILE}.", - exits=1, - ) - - -def get_hash(data, exclude: Iterable[str] = tuple()) -> str: - """Get the hash for a JSON-serializable object. - - data: The data to hash. - exclude (Iterable[str]): Top-level keys to exclude if data is a dict. - RETURNS (str): The hash. - """ - if isinstance(data, dict): - data = {k: v for k, v in data.items() if k not in exclude} - data_str = srsly.json_dumps(data, sort_keys=True).encode("utf8") - return hashlib.md5(data_str).hexdigest() - - -def get_checksum(path: Union[Path, str]) -> str: - """Get the checksum for a file or directory given its file path. If a - directory path is provided, this uses all files in that directory. - - path (Union[Path, str]): The file or directory path. - RETURNS (str): The checksum. - """ - path = Path(path) - if not (path.is_file() or path.is_dir()): - msg.fail(f"Can't get checksum for {path}: not a file or directory", exits=1) - if path.is_file(): - return hashlib.md5(Path(path).read_bytes()).hexdigest() - else: - # TODO: this is currently pretty slow - dir_checksum = hashlib.md5() - for sub_file in sorted(fp for fp in path.rglob("*") if fp.is_file()): - dir_checksum.update(sub_file.read_bytes()) - return dir_checksum.hexdigest() - - @contextmanager def show_validation_error( file_path: Optional[Union[str, Path]] = None, @@ -352,166 +209,10 @@ def import_code(code_path: Optional[Union[Path, str]]) -> None: msg.fail(f"Couldn't load Python code: {code_path}", e, exits=1) -def upload_file(src: Path, dest: Union[str, "FluidPath"]) -> None: - """Upload a file. - - src (Path): The source path. - url (str): The destination URL to upload to. - """ - import smart_open - - # Create parent directories for local paths - if isinstance(dest, Path): - if not dest.parent.exists(): - dest.parent.mkdir(parents=True) - - dest = str(dest) - with smart_open.open(dest, mode="wb") as output_file: - with src.open(mode="rb") as input_file: - output_file.write(input_file.read()) - - -def download_file( - src: Union[str, "FluidPath"], dest: Path, *, force: bool = False -) -> None: - """Download a file using smart_open. - - url (str): The URL of the file. - dest (Path): The destination path. - force (bool): Whether to force download even if file exists. - If False, the download will be skipped. - """ - import smart_open - - if dest.exists() and not force: - return None - src = str(src) - with smart_open.open(src, mode="rb", compression="disable") as input_file: - with dest.open(mode="wb") as output_file: - shutil.copyfileobj(input_file, output_file) - - -def ensure_pathy(path): - """Temporary helper to prevent importing Pathy globally (which can cause - slow and annoying Google Cloud warning).""" - from pathy import Pathy # noqa: F811 - - return Pathy.fluid(path) - - -def git_checkout( - repo: str, subpath: str, dest: Path, *, branch: str = "master", sparse: bool = False -): - git_version = get_git_version() - if dest.exists(): - msg.fail("Destination of checkout must not exist", exits=1) - if not dest.parent.exists(): - msg.fail("Parent of destination of checkout must exist", exits=1) - if sparse and git_version >= (2, 22): - return git_sparse_checkout(repo, subpath, dest, branch) - elif sparse: - # Only show warnings if the user explicitly wants sparse checkout but - # the Git version doesn't support it - err_old = ( - f"You're running an old version of Git (v{git_version[0]}.{git_version[1]}) " - f"that doesn't fully support sparse checkout yet." - ) - err_unk = "You're running an unknown version of Git, so sparse checkout has been disabled." - msg.warn( - f"{err_unk if git_version == (0, 0) else err_old} " - f"This means that more files than necessary may be downloaded " - f"temporarily. To only download the files needed, make sure " - f"you're using Git v2.22 or above." - ) - with make_tempdir() as tmp_dir: - cmd = f"git -C {tmp_dir} clone {repo} . -b {branch}" - run_command(cmd, capture=True) - # We need Path(name) to make sure we also support subdirectories - try: - source_path = tmp_dir / Path(subpath) - if not is_subpath_of(tmp_dir, source_path): - err = f"'{subpath}' is a path outside of the cloned repository." - msg.fail(err, repo, exits=1) - shutil.copytree(str(source_path), str(dest)) - except FileNotFoundError: - err = f"Can't clone {subpath}. Make sure the directory exists in the repo (branch '{branch}')" - msg.fail(err, repo, exits=1) - - -def git_sparse_checkout(repo, subpath, dest, branch): - # We're using Git, partial clone and sparse checkout to - # only clone the files we need - # This ends up being RIDICULOUS. omg. - # So, every tutorial and SO post talks about 'sparse checkout'...But they - # go and *clone* the whole repo. Worthless. And cloning part of a repo - # turns out to be completely broken. The only way to specify a "path" is.. - # a path *on the server*? The contents of which, specifies the paths. Wat. - # Obviously this is hopelessly broken and insecure, because you can query - # arbitrary paths on the server! So nobody enables this. - # What we have to do is disable *all* files. We could then just checkout - # the path, and it'd "work", but be hopelessly slow...Because it goes and - # transfers every missing object one-by-one. So the final piece is that we - # need to use some weird git internals to fetch the missings in bulk, and - # *that* we can do by path. - # We're using Git and sparse checkout to only clone the files we need - with make_tempdir() as tmp_dir: - # This is the "clone, but don't download anything" part. - cmd = ( - f"git clone {repo} {tmp_dir} --no-checkout --depth 1 " - f"-b {branch} --filter=blob:none" - ) - run_command(cmd) - # Now we need to find the missing filenames for the subpath we want. - # Looking for this 'rev-list' command in the git --help? Hah. - cmd = f"git -C {tmp_dir} rev-list --objects --all --missing=print -- {subpath}" - ret = run_command(cmd, capture=True) - git_repo = _http_to_git(repo) - # Now pass those missings into another bit of git internals - missings = " ".join([x[1:] for x in ret.stdout.split() if x.startswith("?")]) - if not missings: - err = ( - f"Could not find any relevant files for '{subpath}'. " - f"Did you specify a correct and complete path within repo '{repo}' " - f"and branch {branch}?" - ) - msg.fail(err, exits=1) - cmd = f"git -C {tmp_dir} fetch-pack {git_repo} {missings}" - run_command(cmd, capture=True) - # And finally, we can checkout our subpath - cmd = f"git -C {tmp_dir} checkout {branch} {subpath}" - run_command(cmd, capture=True) - - # Get a subdirectory of the cloned path, if appropriate - source_path = tmp_dir / Path(subpath) - if not is_subpath_of(tmp_dir, source_path): - err = f"'{subpath}' is a path outside of the cloned repository." - msg.fail(err, repo, exits=1) - - shutil.move(str(source_path), str(dest)) - - -def git_repo_branch_exists(repo: str, branch: str) -> bool: - """Uses 'git ls-remote' to check if a repository and branch exists - - repo (str): URL to get repo. - branch (str): Branch on repo to check. - RETURNS (bool): True if repo:branch exists. - """ - get_git_version() - cmd = f"git ls-remote {repo} {branch}" - # We might be tempted to use `--exit-code` with `git ls-remote`, but - # `run_command` handles the `returncode` for us, so we'll rely on - # the fact that stdout returns '' if the requested branch doesn't exist - ret = run_command(cmd, capture=True) - exists = ret.stdout != "" - return exists - - def get_git_version( error: str = "Could not run 'git'. Make sure it's installed and the executable is available.", ) -> Tuple[int, int]: """Get the version of git and raise an error if calling 'git --version' fails. - error (str): The error message to show. RETURNS (Tuple[int, int]): The version as a (major, minor) tuple. Returns (0, 0) if the version couldn't be determined. @@ -527,30 +228,6 @@ def get_git_version( return int(version[0]), int(version[1]) -def _http_to_git(repo: str) -> str: - if repo.startswith("http://"): - repo = repo.replace(r"http://", r"https://") - if repo.startswith(r"https://"): - repo = repo.replace("https://", "git@").replace("/", ":", 1) - if repo.endswith("/"): - repo = repo[:-1] - repo = f"{repo}.git" - return repo - - -def is_subpath_of(parent, child): - """ - Check whether `child` is a path contained within `parent`. - """ - # Based on https://stackoverflow.com/a/37095733 . - - # In Python 3.9, the `Path.is_relative_to()` method will supplant this, so - # we can stop using crusty old os.path functions. - parent_realpath = os.path.realpath(parent) - child_realpath = os.path.realpath(child) - return os.path.commonpath([parent_realpath, child_realpath]) == parent_realpath - - @overload def string_to_list(value: str, intify: Literal[False] = ...) -> List[str]: ... diff --git a/spacy/cli/project/__init__.py b/spacy/cli/project/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/spacy/cli/project/assets.py b/spacy/cli/project/assets.py deleted file mode 100644 index aa2705986..000000000 --- a/spacy/cli/project/assets.py +++ /dev/null @@ -1,217 +0,0 @@ -import os -import re -import shutil -from pathlib import Path -from typing import Any, Dict, Optional - -import requests -import typer -from wasabi import msg - -from ...util import ensure_path, working_dir -from .._util import ( - PROJECT_FILE, - Arg, - Opt, - SimpleFrozenDict, - download_file, - get_checksum, - get_git_version, - git_checkout, - load_project_config, - parse_config_overrides, - project_cli, -) - -# Whether assets are extra if `extra` is not set. -EXTRA_DEFAULT = False - - -@project_cli.command( - "assets", - context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, -) -def project_assets_cli( - # fmt: off - ctx: typer.Context, # This is only used to read additional arguments - project_dir: Path = Arg(Path.cwd(), help="Path to cloned project. Defaults to current working directory.", exists=True, file_okay=False), - sparse_checkout: bool = Opt(False, "--sparse", "-S", help="Use sparse checkout for assets provided via Git, to only check out and clone the files needed. Requires Git v22.2+."), - extra: bool = Opt(False, "--extra", "-e", help="Download all assets, including those marked as 'extra'.") - # fmt: on -): - """Fetch project assets like datasets and pretrained weights. Assets are - defined in the "assets" section of the project.yml. If a checksum is - provided in the project.yml, the file is only downloaded if no local file - with the same checksum exists. - - DOCS: https://spacy.io/api/cli#project-assets - """ - overrides = parse_config_overrides(ctx.args) - project_assets( - project_dir, - overrides=overrides, - sparse_checkout=sparse_checkout, - extra=extra, - ) - - -def project_assets( - project_dir: Path, - *, - overrides: Dict[str, Any] = SimpleFrozenDict(), - sparse_checkout: bool = False, - extra: bool = False, -) -> None: - """Fetch assets for a project using DVC if possible. - - project_dir (Path): Path to project directory. - sparse_checkout (bool): Use sparse checkout for assets provided via Git, to only check out and clone the files - needed. - extra (bool): Whether to download all assets, including those marked as 'extra'. - """ - project_path = ensure_path(project_dir) - config = load_project_config(project_path, overrides=overrides) - assets = [ - asset - for asset in config.get("assets", []) - if extra or not asset.get("extra", EXTRA_DEFAULT) - ] - if not assets: - msg.warn( - f"No assets specified in {PROJECT_FILE} (if assets are marked as extra, download them with --extra)", - exits=0, - ) - msg.info(f"Fetching {len(assets)} asset(s)") - - for asset in assets: - dest = (project_dir / asset["dest"]).resolve() - checksum = asset.get("checksum") - if "git" in asset: - git_err = ( - f"Cloning spaCy project templates requires Git and the 'git' command. " - f"Make sure it's installed and that the executable is available." - ) - get_git_version(error=git_err) - if dest.exists(): - # If there's already a file, check for checksum - if checksum and checksum == get_checksum(dest): - msg.good( - f"Skipping download with matching checksum: {asset['dest']}" - ) - continue - else: - if dest.is_dir(): - shutil.rmtree(dest) - else: - dest.unlink() - if "repo" not in asset["git"] or asset["git"]["repo"] is None: - msg.fail( - "A git asset must include 'repo', the repository address.", exits=1 - ) - if "path" not in asset["git"] or asset["git"]["path"] is None: - msg.fail( - "A git asset must include 'path' - use \"\" to get the entire repository.", - exits=1, - ) - git_checkout( - asset["git"]["repo"], - asset["git"]["path"], - dest, - branch=asset["git"].get("branch"), - sparse=sparse_checkout, - ) - msg.good(f"Downloaded asset {dest}") - else: - url = asset.get("url") - if not url: - # project.yml defines asset without URL that the user has to place - check_private_asset(dest, checksum) - continue - fetch_asset(project_path, url, dest, checksum) - - -def check_private_asset(dest: Path, checksum: Optional[str] = None) -> None: - """Check and validate assets without a URL (private assets that the user - has to provide themselves) and give feedback about the checksum. - - dest (Path): Destination path of the asset. - checksum (Optional[str]): Optional checksum of the expected file. - """ - if not Path(dest).exists(): - err = f"No URL provided for asset. You need to add this file yourself: {dest}" - msg.warn(err) - else: - if not checksum: - msg.good(f"Asset already exists: {dest}") - elif checksum == get_checksum(dest): - msg.good(f"Asset exists with matching checksum: {dest}") - else: - msg.fail(f"Asset available but with incorrect checksum: {dest}") - - -def fetch_asset( - project_path: Path, url: str, dest: Path, checksum: Optional[str] = None -) -> None: - """Fetch an asset from a given URL or path. If a checksum is provided and a - local file exists, it's only re-downloaded if the checksum doesn't match. - - project_path (Path): Path to project directory. - url (str): URL or path to asset. - checksum (Optional[str]): Optional expected checksum of local file. - RETURNS (Optional[Path]): The path to the fetched asset or None if fetching - the asset failed. - """ - dest_path = (project_path / dest).resolve() - if dest_path.exists(): - # If there's already a file, check for checksum - if checksum: - if checksum == get_checksum(dest_path): - msg.good(f"Skipping download with matching checksum: {dest}") - return - else: - # If there's not a checksum, make sure the file is a possibly valid size - if os.path.getsize(dest_path) == 0: - msg.warn(f"Asset exists but with size of 0 bytes, deleting: {dest}") - os.remove(dest_path) - # We might as well support the user here and create parent directories in - # case the asset dir isn't listed as a dir to create in the project.yml - if not dest_path.parent.exists(): - dest_path.parent.mkdir(parents=True) - with working_dir(project_path): - url = convert_asset_url(url) - try: - download_file(url, dest_path) - msg.good(f"Downloaded asset {dest}") - except requests.exceptions.RequestException as e: - if Path(url).exists() and Path(url).is_file(): - # If it's a local file, copy to destination - shutil.copy(url, str(dest_path)) - msg.good(f"Copied local asset {dest}") - else: - msg.fail(f"Download failed: {dest}", e) - if checksum and checksum != get_checksum(dest_path): - msg.fail(f"Checksum doesn't match value defined in {PROJECT_FILE}: {dest}") - - -def convert_asset_url(url: str) -> str: - """Check and convert the asset URL if needed. - - url (str): The asset URL. - RETURNS (str): The converted URL. - """ - # If the asset URL is a regular GitHub URL it's likely a mistake - if ( - re.match(r"(http(s?)):\/\/github.com", url) - and "releases/download" not in url - and "/raw/" not in url - ): - converted = url.replace("github.com", "raw.githubusercontent.com") - converted = re.sub(r"/(tree|blob)/", "/", converted) - msg.warn( - "Downloading from a regular GitHub URL. This will only download " - "the source of the page, not the actual file. Converting the URL " - "to a raw URL.", - converted, - ) - return converted - return url diff --git a/spacy/cli/project/clone.py b/spacy/cli/project/clone.py deleted file mode 100644 index 2ee27c92a..000000000 --- a/spacy/cli/project/clone.py +++ /dev/null @@ -1,124 +0,0 @@ -import re -import subprocess -from pathlib import Path -from typing import Optional - -from wasabi import msg - -from ... import about -from ...util import ensure_path -from .._util import ( - COMMAND, - PROJECT_FILE, - Arg, - Opt, - get_git_version, - git_checkout, - git_repo_branch_exists, - project_cli, -) - -DEFAULT_REPO = about.__projects__ -DEFAULT_PROJECTS_BRANCH = about.__projects_branch__ -DEFAULT_BRANCHES = ["main", "master"] - - -@project_cli.command("clone") -def project_clone_cli( - # fmt: off - name: str = Arg(..., help="The name of the template to clone"), - dest: Optional[Path] = Arg(None, help="Where to clone the project. Defaults to current working directory", exists=False), - repo: str = Opt(DEFAULT_REPO, "--repo", "-r", help="The repository to clone from"), - branch: Optional[str] = Opt(None, "--branch", "-b", help=f"The branch to clone from. If not provided, will attempt {', '.join(DEFAULT_BRANCHES)}"), - sparse_checkout: bool = Opt(False, "--sparse", "-S", help="Use sparse Git checkout to only check out and clone the files needed. Requires Git v22.2+.") - # fmt: on -): - """Clone a project template from a repository. Calls into "git" and will - only download the files from the given subdirectory. The GitHub repo - defaults to the official spaCy template repo, but can be customized - (including using a private repo). - - DOCS: https://spacy.io/api/cli#project-clone - """ - if dest is None: - dest = Path.cwd() / Path(name).parts[-1] - if repo == DEFAULT_REPO and branch is None: - branch = DEFAULT_PROJECTS_BRANCH - - if branch is None: - for default_branch in DEFAULT_BRANCHES: - if git_repo_branch_exists(repo, default_branch): - branch = default_branch - break - if branch is None: - default_branches_msg = ", ".join(f"'{b}'" for b in DEFAULT_BRANCHES) - msg.fail( - "No branch provided and attempted default " - f"branches {default_branches_msg} do not exist.", - exits=1, - ) - else: - if not git_repo_branch_exists(repo, branch): - msg.fail(f"repo: {repo} (branch: {branch}) does not exist.", exits=1) - assert isinstance(branch, str) - project_clone(name, dest, repo=repo, branch=branch, sparse_checkout=sparse_checkout) - - -def project_clone( - name: str, - dest: Path, - *, - repo: str = about.__projects__, - branch: str = about.__projects_branch__, - sparse_checkout: bool = False, -) -> None: - """Clone a project template from a repository. - - name (str): Name of subdirectory to clone. - dest (Path): Destination path of cloned project. - repo (str): URL of Git repo containing project templates. - branch (str): The branch to clone from - """ - dest = ensure_path(dest) - check_clone(name, dest, repo) - project_dir = dest.resolve() - repo_name = re.sub(r"(http(s?)):\/\/github.com/", "", repo) - try: - git_checkout(repo, name, dest, branch=branch, sparse=sparse_checkout) - except subprocess.CalledProcessError: - err = f"Could not clone '{name}' from repo '{repo_name}' (branch '{branch}')" - msg.fail(err, exits=1) - msg.good(f"Cloned '{name}' from '{repo_name}' (branch '{branch}')", project_dir) - if not (project_dir / PROJECT_FILE).exists(): - msg.warn(f"No {PROJECT_FILE} found in directory") - else: - msg.good(f"Your project is now ready!") - print(f"To fetch the assets, run:\n{COMMAND} project assets {dest}") - - -def check_clone(name: str, dest: Path, repo: str) -> None: - """Check and validate that the destination path can be used to clone. Will - check that Git is available and that the destination path is suitable. - - name (str): Name of the directory to clone from the repo. - dest (Path): Local destination of cloned directory. - repo (str): URL of the repo to clone from. - """ - git_err = ( - f"Cloning spaCy project templates requires Git and the 'git' command. " - f"To clone a project without Git, copy the files from the '{name}' " - f"directory in the {repo} to {dest} manually." - ) - get_git_version(error=git_err) - if not dest: - msg.fail(f"Not a valid directory to clone project: {dest}", exits=1) - if dest.exists(): - # Directory already exists (not allowed, clone needs to create it) - msg.fail(f"Can't clone project, directory already exists: {dest}", exits=1) - if not dest.parent.exists(): - # We're not creating parents, parent dir should exist - msg.fail( - f"Can't clone project, parent directory doesn't exist: {dest.parent}. " - f"Create the necessary folder(s) first before continuing.", - exits=1, - ) diff --git a/spacy/cli/project/document.py b/spacy/cli/project/document.py deleted file mode 100644 index 80107d27a..000000000 --- a/spacy/cli/project/document.py +++ /dev/null @@ -1,115 +0,0 @@ -from pathlib import Path - -from wasabi import MarkdownRenderer, msg - -from ...util import working_dir -from .._util import PROJECT_FILE, Arg, Opt, load_project_config, project_cli - -DOCS_URL = "https://spacy.io" -INTRO_PROJECT = f"""The [`{PROJECT_FILE}`]({PROJECT_FILE}) defines the data assets required by the -project, as well as the available commands and workflows. For details, see the -[spaCy projects documentation]({DOCS_URL}/usage/projects).""" -INTRO_COMMANDS = f"""The following commands are defined by the project. They -can be executed using [`spacy project run [name]`]({DOCS_URL}/api/cli#project-run). -Commands are only re-run if their inputs have changed.""" -INTRO_WORKFLOWS = f"""The following workflows are defined by the project. They -can be executed using [`spacy project run [name]`]({DOCS_URL}/api/cli#project-run) -and will run the specified commands in order. Commands are only re-run if their -inputs have changed.""" -INTRO_ASSETS = f"""The following assets are defined by the project. They can -be fetched by running [`spacy project assets`]({DOCS_URL}/api/cli#project-assets) -in the project directory.""" -# These markers are added to the Markdown and can be used to update the file in -# place if it already exists. Only the auto-generated part will be replaced. -MARKER_START = "" -MARKER_END = "" -# If this marker is used in an existing README, it's ignored and not replaced -MARKER_IGNORE = "" - - -@project_cli.command("document") -def project_document_cli( - # fmt: off - project_dir: Path = Arg(Path.cwd(), help="Path to cloned project. Defaults to current working directory.", exists=True, file_okay=False), - output_file: Path = Opt("-", "--output", "-o", help="Path to output Markdown file for output. Defaults to - for standard output"), - no_emoji: bool = Opt(False, "--no-emoji", "-NE", help="Don't use emoji") - # fmt: on -): - """ - Auto-generate a README.md for a project. If the content is saved to a file, - hidden markers are added so you can add custom content before or after the - auto-generated section and only the auto-generated docs will be replaced - when you re-run the command. - - DOCS: https://spacy.io/api/cli#project-document - """ - project_document(project_dir, output_file, no_emoji=no_emoji) - - -def project_document( - project_dir: Path, output_file: Path, *, no_emoji: bool = False -) -> None: - is_stdout = str(output_file) == "-" - config = load_project_config(project_dir) - md = MarkdownRenderer(no_emoji=no_emoji) - md.add(MARKER_START) - title = config.get("title") - description = config.get("description") - md.add(md.title(1, f"spaCy Project{f': {title}' if title else ''}", "🪐")) - if description: - md.add(description) - md.add(md.title(2, PROJECT_FILE, "📋")) - md.add(INTRO_PROJECT) - # Commands - cmds = config.get("commands", []) - data = [(md.code(cmd["name"]), cmd.get("help", "")) for cmd in cmds] - if data: - md.add(md.title(3, "Commands", "⏯")) - md.add(INTRO_COMMANDS) - md.add(md.table(data, ["Command", "Description"])) - # Workflows - wfs = config.get("workflows", {}).items() - data = [(md.code(n), " → ".join(md.code(w) for w in stp)) for n, stp in wfs] - if data: - md.add(md.title(3, "Workflows", "⏭")) - md.add(INTRO_WORKFLOWS) - md.add(md.table(data, ["Workflow", "Steps"])) - # Assets - assets = config.get("assets", []) - data = [] - for a in assets: - source = "Git" if a.get("git") else "URL" if a.get("url") else "Local" - dest_path = a["dest"] - dest = md.code(dest_path) - if source == "Local": - # Only link assets if they're in the repo - with working_dir(project_dir) as p: - if (p / dest_path).exists(): - dest = md.link(dest, dest_path) - data.append((dest, source, a.get("description", ""))) - if data: - md.add(md.title(3, "Assets", "🗂")) - md.add(INTRO_ASSETS) - md.add(md.table(data, ["File", "Source", "Description"])) - md.add(MARKER_END) - # Output result - if is_stdout: - print(md.text) - else: - content = md.text - if output_file.exists(): - with output_file.open("r", encoding="utf8") as f: - existing = f.read() - if MARKER_IGNORE in existing: - msg.warn("Found ignore marker in existing file: skipping", output_file) - return - if MARKER_START in existing and MARKER_END in existing: - msg.info("Found existing file: only replacing auto-generated docs") - before = existing.split(MARKER_START)[0] - after = existing.split(MARKER_END)[1] - content = f"{before}{content}{after}" - else: - msg.warn("Replacing existing file") - with output_file.open("w", encoding="utf8") as f: - f.write(content) - msg.good("Saved project documentation", output_file) diff --git a/spacy/cli/project/dvc.py b/spacy/cli/project/dvc.py deleted file mode 100644 index 9ad55c433..000000000 --- a/spacy/cli/project/dvc.py +++ /dev/null @@ -1,220 +0,0 @@ -"""This module contains helpers and subcommands for integrating spaCy projects -with Data Version Controk (DVC). https://dvc.org""" -import subprocess -from pathlib import Path -from typing import Any, Dict, Iterable, List, Optional - -from wasabi import msg - -from ...util import ( - SimpleFrozenList, - join_command, - run_command, - split_command, - working_dir, -) -from .._util import ( - COMMAND, - NAME, - PROJECT_FILE, - Arg, - Opt, - get_hash, - load_project_config, - project_cli, -) - -DVC_CONFIG = "dvc.yaml" -DVC_DIR = ".dvc" -UPDATE_COMMAND = "dvc" -DVC_CONFIG_COMMENT = f"""# This file is auto-generated by spaCy based on your {PROJECT_FILE}. If you've -# edited your {PROJECT_FILE}, you can regenerate this file by running: -# {COMMAND} project {UPDATE_COMMAND}""" - - -@project_cli.command(UPDATE_COMMAND) -def project_update_dvc_cli( - # fmt: off - project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False), - workflow: Optional[str] = Arg(None, help=f"Name of workflow defined in {PROJECT_FILE}. Defaults to first workflow if not set."), - verbose: bool = Opt(False, "--verbose", "-V", help="Print more info"), - quiet: bool = Opt(False, "--quiet", "-q", help="Print less info"), - force: bool = Opt(False, "--force", "-F", help="Force update DVC config"), - # fmt: on -): - """Auto-generate Data Version Control (DVC) config. A DVC - project can only define one pipeline, so you need to specify one workflow - defined in the project.yml. If no workflow is specified, the first defined - workflow is used. The DVC config will only be updated if the project.yml - changed. - - DOCS: https://spacy.io/api/cli#project-dvc - """ - project_update_dvc(project_dir, workflow, verbose=verbose, quiet=quiet, force=force) - - -def project_update_dvc( - project_dir: Path, - workflow: Optional[str] = None, - *, - verbose: bool = False, - quiet: bool = False, - force: bool = False, -) -> None: - """Update the auto-generated Data Version Control (DVC) config file. A DVC - project can only define one pipeline, so you need to specify one workflow - defined in the project.yml. Will only update the file if the checksum changed. - - project_dir (Path): The project directory. - workflow (Optional[str]): Optional name of workflow defined in project.yml. - If not set, the first workflow will be used. - verbose (bool): Print more info. - quiet (bool): Print less info. - force (bool): Force update DVC config. - """ - config = load_project_config(project_dir) - updated = update_dvc_config( - project_dir, config, workflow, verbose=verbose, quiet=quiet, force=force - ) - help_msg = "To execute the workflow with DVC, run: dvc repro" - if updated: - msg.good(f"Updated DVC config from {PROJECT_FILE}", help_msg) - else: - msg.info(f"No changes found in {PROJECT_FILE}, no update needed", help_msg) - - -def update_dvc_config( - path: Path, - config: Dict[str, Any], - workflow: Optional[str] = None, - verbose: bool = False, - quiet: bool = False, - force: bool = False, -) -> bool: - """Re-run the DVC commands in dry mode and update dvc.yaml file in the - project directory. The file is auto-generated based on the config. The - first line of the auto-generated file specifies the hash of the config - dict, so if any of the config values change, the DVC config is regenerated. - - path (Path): The path to the project directory. - config (Dict[str, Any]): The loaded project.yml. - verbose (bool): Whether to print additional info (via DVC). - quiet (bool): Don't output anything (via DVC). - force (bool): Force update, even if hashes match. - RETURNS (bool): Whether the DVC config file was updated. - """ - ensure_dvc(path) - workflows = config.get("workflows", {}) - workflow_names = list(workflows.keys()) - check_workflows(workflow_names, workflow) - if not workflow: - workflow = workflow_names[0] - config_hash = get_hash(config) - path = path.resolve() - dvc_config_path = path / DVC_CONFIG - if dvc_config_path.exists(): - # Check if the file was generated using the current config, if not, redo - with dvc_config_path.open("r", encoding="utf8") as f: - ref_hash = f.readline().strip().replace("# ", "") - if ref_hash == config_hash and not force: - return False # Nothing has changed in project.yml, don't need to update - dvc_config_path.unlink() - dvc_commands = [] - config_commands = {cmd["name"]: cmd for cmd in config.get("commands", [])} - - # some flags that apply to every command - flags = [] - if verbose: - flags.append("--verbose") - if quiet: - flags.append("--quiet") - - for name in workflows[workflow]: - command = config_commands[name] - deps = command.get("deps", []) - outputs = command.get("outputs", []) - outputs_no_cache = command.get("outputs_no_cache", []) - if not deps and not outputs and not outputs_no_cache: - continue - # Default to the working dir as the project path since dvc.yaml is auto-generated - # and we don't want arbitrary paths in there - project_cmd = ["python", "-m", NAME, "project", "run", name] - deps_cmd = [c for cl in [["-d", p] for p in deps] for c in cl] - outputs_cmd = [c for cl in [["-o", p] for p in outputs] for c in cl] - outputs_nc_cmd = [c for cl in [["-O", p] for p in outputs_no_cache] for c in cl] - - dvc_cmd = ["run", *flags, "-n", name, "-w", str(path), "--no-exec"] - if command.get("no_skip"): - dvc_cmd.append("--always-changed") - full_cmd = [*dvc_cmd, *deps_cmd, *outputs_cmd, *outputs_nc_cmd, *project_cmd] - dvc_commands.append(join_command(full_cmd)) - - if not dvc_commands: - # If we don't check for this, then there will be an error when reading the - # config, since DVC wouldn't create it. - msg.fail( - "No usable commands for DVC found. This can happen if none of your " - "commands have dependencies or outputs.", - exits=1, - ) - - with working_dir(path): - for c in dvc_commands: - dvc_command = "dvc " + c - run_command(dvc_command) - with dvc_config_path.open("r+", encoding="utf8") as f: - content = f.read() - f.seek(0, 0) - f.write(f"# {config_hash}\n{DVC_CONFIG_COMMENT}\n{content}") - return True - - -def check_workflows(workflows: List[str], workflow: Optional[str] = None) -> None: - """Validate workflows provided in project.yml and check that a given - workflow can be used to generate a DVC config. - - workflows (List[str]): Names of the available workflows. - workflow (Optional[str]): The name of the workflow to convert. - """ - if not workflows: - msg.fail( - f"No workflows defined in {PROJECT_FILE}. To generate a DVC config, " - f"define at least one list of commands.", - exits=1, - ) - if workflow is not None and workflow not in workflows: - msg.fail( - f"Workflow '{workflow}' not defined in {PROJECT_FILE}. " - f"Available workflows: {', '.join(workflows)}", - exits=1, - ) - if not workflow: - msg.warn( - f"No workflow specified for DVC pipeline. Using the first workflow " - f"defined in {PROJECT_FILE}: '{workflows[0]}'" - ) - - -def ensure_dvc(project_dir: Path) -> None: - """Ensure that the "dvc" command is available and that the current project - directory is an initialized DVC project. - """ - try: - subprocess.run(["dvc", "--version"], stdout=subprocess.DEVNULL) - except Exception: - msg.fail( - "To use spaCy projects with DVC (Data Version Control), DVC needs " - "to be installed and the 'dvc' command needs to be available", - "You can install the Python package from pip (pip install dvc) or " - "conda (conda install -c conda-forge dvc). For more details, see the " - "documentation: https://dvc.org/doc/install", - exits=1, - ) - if not (project_dir / ".dvc").exists(): - msg.fail( - "Project not initialized as a DVC project", - "To initialize a DVC project, you can run 'dvc init' in the project " - "directory. For more details, see the documentation: " - "https://dvc.org/doc/command-reference/init", - exits=1, - ) diff --git a/spacy/cli/project/pull.py b/spacy/cli/project/pull.py deleted file mode 100644 index e9be74df7..000000000 --- a/spacy/cli/project/pull.py +++ /dev/null @@ -1,67 +0,0 @@ -from pathlib import Path - -from wasabi import msg - -from .._util import Arg, load_project_config, logger, project_cli -from .remote_storage import RemoteStorage, get_command_hash -from .run import update_lockfile - - -@project_cli.command("pull") -def project_pull_cli( - # fmt: off - remote: str = Arg("default", help="Name or path of remote storage"), - project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False), - # fmt: on -): - """Retrieve available precomputed outputs from a remote storage. - You can alias remotes in your project.yml by mapping them to storage paths. - A storage can be anything that the smart-open library can upload to, e.g. - AWS, Google Cloud Storage, SSH, local directories etc. - - DOCS: https://spacy.io/api/cli#project-pull - """ - for url, output_path in project_pull(project_dir, remote): - if url is not None: - msg.good(f"Pulled {output_path} from {url}") - - -def project_pull(project_dir: Path, remote: str, *, verbose: bool = False): - # TODO: We don't have tests for this :(. It would take a bit of mockery to - # set up. I guess see if it breaks first? - config = load_project_config(project_dir) - if remote in config.get("remotes", {}): - remote = config["remotes"][remote] - storage = RemoteStorage(project_dir, remote) - commands = list(config.get("commands", [])) - # We use a while loop here because we don't know how the commands - # will be ordered. A command might need dependencies from one that's later - # in the list. - while commands: - for i, cmd in enumerate(list(commands)): - logger.debug("CMD: %s.", cmd["name"]) - deps = [project_dir / dep for dep in cmd.get("deps", [])] - if all(dep.exists() for dep in deps): - cmd_hash = get_command_hash("", "", deps, cmd["script"]) - for output_path in cmd.get("outputs", []): - url = storage.pull(output_path, command_hash=cmd_hash) - logger.debug( - "URL: %s for %s with command hash %s", - url, - output_path, - cmd_hash, - ) - yield url, output_path - - out_locs = [project_dir / out for out in cmd.get("outputs", [])] - if all(loc.exists() for loc in out_locs): - update_lockfile(project_dir, cmd) - # We remove the command from the list here, and break, so that - # we iterate over the loop again. - commands.pop(i) - break - else: - logger.debug("Dependency missing. Skipping %s outputs.", cmd["name"]) - else: - # If we didn't break the for loop, break the while loop. - break diff --git a/spacy/cli/project/push.py b/spacy/cli/project/push.py deleted file mode 100644 index a7915e547..000000000 --- a/spacy/cli/project/push.py +++ /dev/null @@ -1,69 +0,0 @@ -from pathlib import Path - -from wasabi import msg - -from .._util import Arg, load_project_config, logger, project_cli -from .remote_storage import RemoteStorage, get_command_hash, get_content_hash - - -@project_cli.command("push") -def project_push_cli( - # fmt: off - remote: str = Arg("default", help="Name or path of remote storage"), - project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False), - # fmt: on -): - """Persist outputs to a remote storage. You can alias remotes in your - project.yml by mapping them to storage paths. A storage can be anything that - the smart-open library can upload to, e.g. AWS, Google Cloud Storage, SSH, - local directories etc. - - DOCS: https://spacy.io/api/cli#project-push - """ - for output_path, url in project_push(project_dir, remote): - if url is None: - msg.info(f"Skipping {output_path}") - else: - msg.good(f"Pushed {output_path} to {url}") - - -def project_push(project_dir: Path, remote: str): - """Persist outputs to a remote storage. You can alias remotes in your project.yml - by mapping them to storage paths. A storage can be anything that the smart-open - library can upload to, e.g. gcs, aws, ssh, local directories etc - """ - config = load_project_config(project_dir) - if remote in config.get("remotes", {}): - remote = config["remotes"][remote] - storage = RemoteStorage(project_dir, remote) - for cmd in config.get("commands", []): - logger.debug("CMD: %s", cmd["name"]) - deps = [project_dir / dep for dep in cmd.get("deps", [])] - if any(not dep.exists() for dep in deps): - logger.debug("Dependency missing. Skipping %s outputs", cmd["name"]) - continue - cmd_hash = get_command_hash( - "", "", [project_dir / dep for dep in cmd.get("deps", [])], cmd["script"] - ) - logger.debug("CMD_HASH: %s", cmd_hash) - for output_path in cmd.get("outputs", []): - output_loc = project_dir / output_path - if output_loc.exists() and _is_not_empty_dir(output_loc): - url = storage.push( - output_path, - command_hash=cmd_hash, - content_hash=get_content_hash(output_loc), - ) - logger.debug( - "URL: %s for output %s with cmd_hash %s", url, output_path, cmd_hash - ) - yield output_path, url - - -def _is_not_empty_dir(loc: Path): - if not loc.is_dir(): - return True - elif any(_is_not_empty_dir(child) for child in loc.iterdir()): - return True - else: - return False diff --git a/spacy/cli/project/remote_storage.py b/spacy/cli/project/remote_storage.py deleted file mode 100644 index 84235a90d..000000000 --- a/spacy/cli/project/remote_storage.py +++ /dev/null @@ -1,212 +0,0 @@ -import hashlib -import os -import site -import tarfile -import urllib.parse -from pathlib import Path -from typing import TYPE_CHECKING, Dict, List, Optional - -from wasabi import msg - -from ... import about -from ...errors import Errors -from ...git_info import GIT_VERSION -from ...util import ENV_VARS, check_bool_env_var, get_minor_version -from .._util import ( - download_file, - ensure_pathy, - get_checksum, - get_hash, - make_tempdir, - upload_file, -) - -if TYPE_CHECKING: - from pathy import FluidPath # noqa: F401 - - -class RemoteStorage: - """Push and pull outputs to and from a remote file storage. - - Remotes can be anything that `smart-open` can support: AWS, GCS, file system, - ssh, etc. - """ - - def __init__(self, project_root: Path, url: str, *, compression="gz"): - self.root = project_root - self.url = ensure_pathy(url) - self.compression = compression - - def push(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath": - """Compress a file or directory within a project and upload it to a remote - storage. If an object exists at the full URL, nothing is done. - - Within the remote storage, files are addressed by their project path - (url encoded) and two user-supplied hashes, representing their creation - context and their file contents. If the URL already exists, the data is - not uploaded. Paths are archived and compressed prior to upload. - """ - loc = self.root / path - if not loc.exists(): - raise IOError(f"Cannot push {loc}: does not exist.") - url = self.make_url(path, command_hash, content_hash) - if url.exists(): - return url - tmp: Path - with make_tempdir() as tmp: - tar_loc = tmp / self.encode_name(str(path)) - mode_string = f"w:{self.compression}" if self.compression else "w" - with tarfile.open(tar_loc, mode=mode_string) as tar_file: - tar_file.add(str(loc), arcname=str(path)) - upload_file(tar_loc, url) - return url - - def pull( - self, - path: Path, - *, - command_hash: Optional[str] = None, - content_hash: Optional[str] = None, - ) -> Optional["FluidPath"]: - """Retrieve a file from the remote cache. If the file already exists, - nothing is done. - - If the command_hash and/or content_hash are specified, only matching - results are returned. If no results are available, an error is raised. - """ - dest = self.root / path - if dest.exists(): - return None - url = self.find(path, command_hash=command_hash, content_hash=content_hash) - if url is None: - return url - else: - # Make sure the destination exists - if not dest.parent.exists(): - dest.parent.mkdir(parents=True) - tmp: Path - with make_tempdir() as tmp: - tar_loc = tmp / url.parts[-1] - download_file(url, tar_loc) - mode_string = f"r:{self.compression}" if self.compression else "r" - with tarfile.open(tar_loc, mode=mode_string) as tar_file: - # This requires that the path is added correctly, relative - # to root. This is how we set things up in push() - - # Disallow paths outside the current directory for the tar - # file (CVE-2007-4559, directory traversal vulnerability) - def is_within_directory(directory, target): - abs_directory = os.path.abspath(directory) - abs_target = os.path.abspath(target) - prefix = os.path.commonprefix([abs_directory, abs_target]) - return prefix == abs_directory - - def safe_extract(tar, path): - for member in tar.getmembers(): - member_path = os.path.join(path, member.name) - if not is_within_directory(path, member_path): - raise ValueError(Errors.E852) - tar.extractall(path) - - safe_extract(tar_file, self.root) - return url - - def find( - self, - path: Path, - *, - command_hash: Optional[str] = None, - content_hash: Optional[str] = None, - ) -> Optional["FluidPath"]: - """Find the best matching version of a file within the storage, - or `None` if no match can be found. If both the creation and content hash - are specified, only exact matches will be returned. Otherwise, the most - recent matching file is preferred. - """ - name = self.encode_name(str(path)) - urls = [] - if command_hash is not None and content_hash is not None: - url = self.url / name / command_hash / content_hash - urls = [url] if url.exists() else [] - elif command_hash is not None: - if (self.url / name / command_hash).exists(): - urls = list((self.url / name / command_hash).iterdir()) - else: - if (self.url / name).exists(): - for sub_dir in (self.url / name).iterdir(): - urls.extend(sub_dir.iterdir()) - if content_hash is not None: - urls = [url for url in urls if url.parts[-1] == content_hash] - if len(urls) >= 2: - try: - urls.sort(key=lambda x: x.stat().last_modified) # type: ignore - except Exception: - msg.warn( - "Unable to sort remote files by last modified. The file(s) " - "pulled from the cache may not be the most recent." - ) - return urls[-1] if urls else None - - def make_url(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath": - """Construct a URL from a subpath, a creation hash and a content hash.""" - return self.url / self.encode_name(str(path)) / command_hash / content_hash - - def encode_name(self, name: str) -> str: - """Encode a subpath into a URL-safe name.""" - return urllib.parse.quote_plus(name) - - -def get_content_hash(loc: Path) -> str: - return get_checksum(loc) - - -def get_command_hash( - site_hash: str, env_hash: str, deps: List[Path], cmd: List[str] -) -> str: - """Create a hash representing the execution of a command. This includes the - currently installed packages, whatever environment variables have been marked - as relevant, and the command. - """ - if check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION): - spacy_v = GIT_VERSION - else: - spacy_v = str(get_minor_version(about.__version__) or "") - dep_checksums = [get_checksum(dep) for dep in sorted(deps)] - hashes = [spacy_v, site_hash, env_hash] + dep_checksums - hashes.extend(cmd) - creation_bytes = "".join(hashes).encode("utf8") - return hashlib.md5(creation_bytes).hexdigest() - - -def get_site_hash(): - """Hash the current Python environment's site-packages contents, including - the name and version of the libraries. The list we're hashing is what - `pip freeze` would output. - """ - site_dirs = site.getsitepackages() - if site.ENABLE_USER_SITE: - site_dirs.extend(site.getusersitepackages()) - packages = set() - for site_dir in site_dirs: - site_dir = Path(site_dir) - for subpath in site_dir.iterdir(): - if subpath.parts[-1].endswith("dist-info"): - packages.add(subpath.parts[-1].replace(".dist-info", "")) - package_bytes = "".join(sorted(packages)).encode("utf8") - return hashlib.md5sum(package_bytes).hexdigest() - - -def get_env_hash(env: Dict[str, str]) -> str: - """Construct a hash of the environment variables that will be passed into - the commands. - - Values in the env dict may be references to the current os.environ, using - the syntax $ENV_VAR to mean os.environ[ENV_VAR] - """ - env_vars = {} - for key, value in env.items(): - if value.startswith("$"): - env_vars[key] = os.environ.get(value[1:], "") - else: - env_vars[key] = value - return get_hash(env_vars) diff --git a/spacy/cli/project/run.py b/spacy/cli/project/run.py deleted file mode 100644 index 43972a202..000000000 --- a/spacy/cli/project/run.py +++ /dev/null @@ -1,379 +0,0 @@ -import os.path -import sys -from pathlib import Path -from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple - -import srsly -import typer -from wasabi import msg -from wasabi.util import locale_escape - -from ... import about -from ...git_info import GIT_VERSION -from ...util import ( - ENV_VARS, - SimpleFrozenDict, - SimpleFrozenList, - check_bool_env_var, - is_cwd, - is_minor_version_match, - join_command, - run_command, - split_command, - working_dir, -) -from .._util import ( - COMMAND, - PROJECT_FILE, - PROJECT_LOCK, - Arg, - Opt, - get_checksum, - get_hash, - load_project_config, - parse_config_overrides, - project_cli, -) - - -@project_cli.command( - "run", context_settings={"allow_extra_args": True, "ignore_unknown_options": True} -) -def project_run_cli( - # fmt: off - ctx: typer.Context, # This is only used to read additional arguments - subcommand: str = Arg(None, help=f"Name of command defined in the {PROJECT_FILE}"), - project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False), - force: bool = Opt(False, "--force", "-F", help="Force re-running steps, even if nothing changed"), - dry: bool = Opt(False, "--dry", "-D", help="Perform a dry run and don't execute scripts"), - show_help: bool = Opt(False, "--help", help="Show help message and available subcommands") - # fmt: on -): - """Run a named command or workflow defined in the project.yml. If a workflow - name is specified, all commands in the workflow are run, in order. If - commands define dependencies and/or outputs, they will only be re-run if - state has changed. - - DOCS: https://spacy.io/api/cli#project-run - """ - if show_help or not subcommand: - print_run_help(project_dir, subcommand) - else: - overrides = parse_config_overrides(ctx.args) - project_run(project_dir, subcommand, overrides=overrides, force=force, dry=dry) - - -def project_run( - project_dir: Path, - subcommand: str, - *, - overrides: Dict[str, Any] = SimpleFrozenDict(), - force: bool = False, - dry: bool = False, - capture: bool = False, - skip_requirements_check: bool = False, -) -> None: - """Run a named script defined in the project.yml. If the script is part - of the default pipeline (defined in the "run" section), DVC is used to - execute the command, so it can determine whether to rerun it. It then - calls into "exec" to execute it. - - project_dir (Path): Path to project directory. - subcommand (str): Name of command to run. - overrides (Dict[str, Any]): Optional config overrides. - force (bool): Force re-running, even if nothing changed. - dry (bool): Perform a dry run and don't execute commands. - capture (bool): Whether to capture the output and errors of individual commands. - If False, the stdout and stderr will not be redirected, and if there's an error, - sys.exit will be called with the return code. You should use capture=False - when you want to turn over execution to the command, and capture=True - when you want to run the command more like a function. - skip_requirements_check (bool): Whether to skip the requirements check. - """ - config = load_project_config(project_dir, overrides=overrides) - commands = {cmd["name"]: cmd for cmd in config.get("commands", [])} - workflows = config.get("workflows", {}) - validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand) - - req_path = project_dir / "requirements.txt" - if not skip_requirements_check: - if config.get("check_requirements", True) and os.path.exists(req_path): - with req_path.open() as requirements_file: - _check_requirements([req.strip() for req in requirements_file]) - - if subcommand in workflows: - msg.info(f"Running workflow '{subcommand}'") - for cmd in workflows[subcommand]: - project_run( - project_dir, - cmd, - overrides=overrides, - force=force, - dry=dry, - capture=capture, - skip_requirements_check=True, - ) - else: - cmd = commands[subcommand] - for dep in cmd.get("deps", []): - if not (project_dir / dep).exists(): - err = f"Missing dependency specified by command '{subcommand}': {dep}" - err_help = "Maybe you forgot to run the 'project assets' command or a previous step?" - err_exits = 1 if not dry else None - msg.fail(err, err_help, exits=err_exits) - check_spacy_commit = check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION) - with working_dir(project_dir) as current_dir: - msg.divider(subcommand) - rerun = check_rerun(current_dir, cmd, check_spacy_commit=check_spacy_commit) - if not rerun and not force: - msg.info(f"Skipping '{cmd['name']}': nothing changed") - else: - run_commands(cmd["script"], dry=dry, capture=capture) - if not dry: - update_lockfile(current_dir, cmd) - - -def print_run_help(project_dir: Path, subcommand: Optional[str] = None) -> None: - """Simulate a CLI help prompt using the info available in the project.yml. - - project_dir (Path): The project directory. - subcommand (Optional[str]): The subcommand or None. If a subcommand is - provided, the subcommand help is shown. Otherwise, the top-level help - and a list of available commands is printed. - """ - config = load_project_config(project_dir) - config_commands = config.get("commands", []) - commands = {cmd["name"]: cmd for cmd in config_commands} - workflows = config.get("workflows", {}) - project_loc = "" if is_cwd(project_dir) else project_dir - if subcommand: - validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand) - print(f"Usage: {COMMAND} project run {subcommand} {project_loc}") - if subcommand in commands: - help_text = commands[subcommand].get("help") - if help_text: - print(f"\n{help_text}\n") - elif subcommand in workflows: - steps = workflows[subcommand] - print(f"\nWorkflow consisting of {len(steps)} commands:") - steps_data = [ - (f"{i + 1}. {step}", commands[step].get("help", "")) - for i, step in enumerate(steps) - ] - msg.table(steps_data) - help_cmd = f"{COMMAND} project run [COMMAND] {project_loc} --help" - print(f"For command details, run: {help_cmd}") - else: - print("") - title = config.get("title") - if title: - print(f"{locale_escape(title)}\n") - if config_commands: - print(f"Available commands in {PROJECT_FILE}") - print(f"Usage: {COMMAND} project run [COMMAND] {project_loc}") - msg.table([(cmd["name"], cmd.get("help", "")) for cmd in config_commands]) - if workflows: - print(f"Available workflows in {PROJECT_FILE}") - print(f"Usage: {COMMAND} project run [WORKFLOW] {project_loc}") - msg.table([(name, " -> ".join(steps)) for name, steps in workflows.items()]) - - -def run_commands( - commands: Iterable[str] = SimpleFrozenList(), - silent: bool = False, - dry: bool = False, - capture: bool = False, -) -> None: - """Run a sequence of commands in a subprocess, in order. - - commands (List[str]): The string commands. - silent (bool): Don't print the commands. - dry (bool): Perform a dry run and don't execut anything. - capture (bool): Whether to capture the output and errors of individual commands. - If False, the stdout and stderr will not be redirected, and if there's an error, - sys.exit will be called with the return code. You should use capture=False - when you want to turn over execution to the command, and capture=True - when you want to run the command more like a function. - """ - for c in commands: - command = split_command(c) - # Not sure if this is needed or a good idea. Motivation: users may often - # use commands in their config that reference "python" and we want to - # make sure that it's always executing the same Python that spaCy is - # executed with and the pip in the same env, not some other Python/pip. - # Also ensures cross-compatibility if user 1 writes "python3" (because - # that's how it's set up on their system), and user 2 without the - # shortcut tries to re-run the command. - if len(command) and command[0] in ("python", "python3"): - command[0] = sys.executable - elif len(command) and command[0] in ("pip", "pip3"): - command = [sys.executable, "-m", "pip", *command[1:]] - if not silent: - print(f"Running command: {join_command(command)}") - if not dry: - run_command(command, capture=capture) - - -def validate_subcommand( - commands: Sequence[str], workflows: Sequence[str], subcommand: str -) -> None: - """Check that a subcommand is valid and defined. Raises an error otherwise. - - commands (Sequence[str]): The available commands. - subcommand (str): The subcommand. - """ - if not commands and not workflows: - msg.fail(f"No commands or workflows defined in {PROJECT_FILE}", exits=1) - if subcommand not in commands and subcommand not in workflows: - help_msg = [] - if subcommand in ["assets", "asset"]: - help_msg.append("Did you mean to run: python -m spacy project assets?") - if commands: - help_msg.append(f"Available commands: {', '.join(commands)}") - if workflows: - help_msg.append(f"Available workflows: {', '.join(workflows)}") - msg.fail( - f"Can't find command or workflow '{subcommand}' in {PROJECT_FILE}", - ". ".join(help_msg), - exits=1, - ) - - -def check_rerun( - project_dir: Path, - command: Dict[str, Any], - *, - check_spacy_version: bool = True, - check_spacy_commit: bool = False, -) -> bool: - """Check if a command should be rerun because its settings or inputs/outputs - changed. - - project_dir (Path): The current project directory. - command (Dict[str, Any]): The command, as defined in the project.yml. - strict_version (bool): - RETURNS (bool): Whether to re-run the command. - """ - # Always rerun if no-skip is set - if command.get("no_skip", False): - return True - lock_path = project_dir / PROJECT_LOCK - if not lock_path.exists(): # We don't have a lockfile, run command - return True - data = srsly.read_yaml(lock_path) - if command["name"] not in data: # We don't have info about this command - return True - entry = data[command["name"]] - # Always run commands with no outputs (otherwise they'd always be skipped) - if not entry.get("outs", []): - return True - # Always rerun if spaCy version or commit hash changed - spacy_v = entry.get("spacy_version") - commit = entry.get("spacy_git_version") - if check_spacy_version and not is_minor_version_match(spacy_v, about.__version__): - info = f"({spacy_v} in {PROJECT_LOCK}, {about.__version__} current)" - msg.info(f"Re-running '{command['name']}': spaCy minor version changed {info}") - return True - if check_spacy_commit and commit != GIT_VERSION: - info = f"({commit} in {PROJECT_LOCK}, {GIT_VERSION} current)" - msg.info(f"Re-running '{command['name']}': spaCy commit changed {info}") - return True - # If the entry in the lockfile matches the lockfile entry that would be - # generated from the current command, we don't rerun because it means that - # all inputs/outputs, hashes and scripts are the same and nothing changed - lock_entry = get_lock_entry(project_dir, command) - exclude = ["spacy_version", "spacy_git_version"] - return get_hash(lock_entry, exclude=exclude) != get_hash(entry, exclude=exclude) - - -def update_lockfile(project_dir: Path, command: Dict[str, Any]) -> None: - """Update the lockfile after running a command. Will create a lockfile if - it doesn't yet exist and will add an entry for the current command, its - script and dependencies/outputs. - - project_dir (Path): The current project directory. - command (Dict[str, Any]): The command, as defined in the project.yml. - """ - lock_path = project_dir / PROJECT_LOCK - if not lock_path.exists(): - srsly.write_yaml(lock_path, {}) - data = {} - else: - data = srsly.read_yaml(lock_path) - data[command["name"]] = get_lock_entry(project_dir, command) - srsly.write_yaml(lock_path, data) - - -def get_lock_entry(project_dir: Path, command: Dict[str, Any]) -> Dict[str, Any]: - """Get a lockfile entry for a given command. An entry includes the command, - the script (command steps) and a list of dependencies and outputs with - their paths and file hashes, if available. The format is based on the - dvc.lock files, to keep things consistent. - - project_dir (Path): The current project directory. - command (Dict[str, Any]): The command, as defined in the project.yml. - RETURNS (Dict[str, Any]): The lockfile entry. - """ - deps = get_fileinfo(project_dir, command.get("deps", [])) - outs = get_fileinfo(project_dir, command.get("outputs", [])) - outs_nc = get_fileinfo(project_dir, command.get("outputs_no_cache", [])) - return { - "cmd": f"{COMMAND} run {command['name']}", - "script": command["script"], - "deps": deps, - "outs": [*outs, *outs_nc], - "spacy_version": about.__version__, - "spacy_git_version": GIT_VERSION, - } - - -def get_fileinfo(project_dir: Path, paths: List[str]) -> List[Dict[str, Optional[str]]]: - """Generate the file information for a list of paths (dependencies, outputs). - Includes the file path and the file's checksum. - - project_dir (Path): The current project directory. - paths (List[str]): The file paths. - RETURNS (List[Dict[str, str]]): The lockfile entry for a file. - """ - data = [] - for path in paths: - file_path = project_dir / path - md5 = get_checksum(file_path) if file_path.exists() else None - data.append({"path": path, "md5": md5}) - return data - - -def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]: - """Checks whether requirements are installed and free of version conflicts. - requirements (List[str]): List of requirements. - RETURNS (Tuple[bool, bool]): Whether (1) any packages couldn't be imported, (2) any packages with version conflicts - exist. - """ - import pkg_resources - - failed_pkgs_msgs: List[str] = [] - conflicting_pkgs_msgs: List[str] = [] - - for req in requirements: - try: - pkg_resources.require(req) - except pkg_resources.DistributionNotFound as dnf: - failed_pkgs_msgs.append(dnf.report()) - except pkg_resources.VersionConflict as vc: - conflicting_pkgs_msgs.append(vc.report()) - except Exception: - msg.warn( - f"Unable to check requirement: {req} " - "Checks are currently limited to requirement specifiers " - "(PEP 508)" - ) - - if len(failed_pkgs_msgs) or len(conflicting_pkgs_msgs): - msg.warn( - title="Missing requirements or requirement conflicts detected. Make sure your Python environment is set up " - "correctly and you installed all requirements specified in your project's requirements.txt: " - ) - for pgk_msg in failed_pkgs_msgs + conflicting_pkgs_msgs: - msg.text(pgk_msg) - - return len(failed_pkgs_msgs) > 0, len(conflicting_pkgs_msgs) > 0 diff --git a/spacy/errors.py b/spacy/errors.py index a2f8ca85c..225cb9c86 100644 --- a/spacy/errors.py +++ b/spacy/errors.py @@ -557,8 +557,6 @@ class Errors(metaclass=ErrorsWithCodes): "floret vectors, not {mode} vectors.") E851 = ("The 'textcat' component labels should only have values of 0 or 1, " "but found value of '{val}'.") - E852 = ("The tar file pulled from the remote attempted an unsafe path " - "traversal.") E853 = ("Unsupported component factory name '{name}'. The character '.' is " "not permitted in factory names.") E854 = ("Unable to set doc.ents. Check that the 'ents_filter' does not " diff --git a/spacy/schemas.py b/spacy/schemas.py index 22f45372c..22c25e99d 100644 --- a/spacy/schemas.py +++ b/spacy/schemas.py @@ -465,66 +465,6 @@ CONFIG_SCHEMAS = { "initialize": ConfigSchemaInit, } - -# Project config Schema - - -class ProjectConfigAssetGitItem(BaseModel): - # fmt: off - repo: StrictStr = Field(..., title="URL of Git repo to download from") - path: StrictStr = Field(..., title="File path or sub-directory to download (used for sparse checkout)") - branch: StrictStr = Field("master", title="Branch to clone from") - # fmt: on - - -class ProjectConfigAssetURL(BaseModel): - # fmt: off - dest: StrictStr = Field(..., title="Destination of downloaded asset") - url: Optional[StrictStr] = Field(None, title="URL of asset") - checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})") - description: StrictStr = Field("", title="Description of asset") - # fmt: on - - -class ProjectConfigAssetGit(BaseModel): - # fmt: off - git: ProjectConfigAssetGitItem = Field(..., title="Git repo information") - checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})") - description: Optional[StrictStr] = Field(None, title="Description of asset") - # fmt: on - - -class ProjectConfigCommand(BaseModel): - # fmt: off - name: StrictStr = Field(..., title="Name of command") - help: Optional[StrictStr] = Field(None, title="Command description") - script: List[StrictStr] = Field([], title="List of CLI commands to run, in order") - deps: List[StrictStr] = Field([], title="File dependencies required by this command") - outputs: List[StrictStr] = Field([], title="Outputs produced by this command") - outputs_no_cache: List[StrictStr] = Field([], title="Outputs not tracked by DVC (DVC only)") - no_skip: bool = Field(False, title="Never skip this command, even if nothing changed") - # fmt: on - - class Config: - title = "A single named command specified in a project config" - extra = "forbid" - - -class ProjectConfigSchema(BaseModel): - # fmt: off - vars: Dict[StrictStr, Any] = Field({}, title="Optional variables to substitute in commands") - env: Dict[StrictStr, Any] = Field({}, title="Optional variable names to substitute in commands, mapped to environment variable names") - assets: List[Union[ProjectConfigAssetURL, ProjectConfigAssetGit]] = Field([], title="Data assets") - workflows: Dict[StrictStr, List[StrictStr]] = Field({}, title="Named workflows, mapped to list of project commands to run in order") - commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts") - title: Optional[str] = Field(None, title="Project title") - spacy_version: Optional[StrictStr] = Field(None, title="spaCy version range that the project is compatible with") - # fmt: on - - class Config: - title = "Schema for project configuration file" - - # Recommendations for init config workflows diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py index f5a7aadb8..9b4f6851e 100644 --- a/spacy/tests/test_cli.py +++ b/spacy/tests/test_cli.py @@ -11,21 +11,13 @@ import srsly from click import NoSuchOption from packaging.specifiers import SpecifierSet from thinc.api import Config, ConfigValidationError +from weasel.cli.remote_storage import RemoteStorage +from weasel.cli.run import _check_requirements import spacy from spacy import about from spacy.cli import info -from spacy.cli._util import ( - download_file, - is_subpath_of, - load_project_config, - parse_config_overrides, - string_to_list, - substitute_project_variables, - upload_file, - validate_project_commands, - walk_directory, -) +from spacy.cli._util import parse_config_overrides, string_to_list, walk_directory from spacy.cli.apply import apply from spacy.cli.debug_data import ( _compile_gold, @@ -43,13 +35,11 @@ from spacy.cli.find_threshold import find_threshold from spacy.cli.init_config import RECOMMENDATIONS, fill_config, init_config from spacy.cli.init_pipeline import _init_labels from spacy.cli.package import _is_permitted_package_name, get_third_party_dependencies -from spacy.cli.project.remote_storage import RemoteStorage -from spacy.cli.project.run import _check_requirements from spacy.cli.validate import get_model_pkgs from spacy.lang.en import English from spacy.lang.nl import Dutch from spacy.language import Language -from spacy.schemas import ProjectConfigSchema, RecommendationSchema, validate +from spacy.schemas import RecommendationSchema, validate from spacy.tokens import Doc, DocBin from spacy.tokens.span import Span from spacy.training import Example, docs_to_json, offsets_to_biluo_tags @@ -134,25 +124,6 @@ def test_issue7055(): assert "model" in filled_cfg["components"]["ner"] -@pytest.mark.issue(11235) -def test_issue11235(): - """ - Test that the cli handles interpolation in the directory names correctly when loading project config. - """ - lang_var = "en" - variables = {"lang": lang_var} - commands = [{"name": "x", "script": ["hello ${vars.lang}"]}] - directories = ["cfg", "${vars.lang}_model"] - project = {"commands": commands, "vars": variables, "directories": directories} - with make_tempdir() as d: - srsly.write_yaml(d / "project.yml", project) - cfg = load_project_config(d) - # Check that the directories are interpolated and created correctly - assert os.path.exists(d / "cfg") - assert os.path.exists(d / f"{lang_var}_model") - assert cfg["commands"][0]["script"][0] == f"hello {lang_var}" - - @pytest.mark.issue(12566) @pytest.mark.parametrize( "factory,output_file", @@ -443,136 +414,6 @@ def test_cli_converters_conll_ner_to_docs(): assert ent.text in ["New York City", "London"] -def test_project_config_validation_full(): - config = { - "vars": {"some_var": 20}, - "directories": ["assets", "configs", "corpus", "scripts", "training"], - "assets": [ - { - "dest": "x", - "extra": True, - "url": "https://example.com", - "checksum": "63373dd656daa1fd3043ce166a59474c", - }, - { - "dest": "y", - "git": { - "repo": "https://github.com/example/repo", - "branch": "develop", - "path": "y", - }, - }, - { - "dest": "z", - "extra": False, - "url": "https://example.com", - "checksum": "63373dd656daa1fd3043ce166a59474c", - }, - ], - "commands": [ - { - "name": "train", - "help": "Train a model", - "script": ["python -m spacy train config.cfg -o training"], - "deps": ["config.cfg", "corpus/training.spcy"], - "outputs": ["training/model-best"], - }, - {"name": "test", "script": ["pytest", "custom.py"], "no_skip": True}, - ], - "workflows": {"all": ["train", "test"], "train": ["train"]}, - } - errors = validate(ProjectConfigSchema, config) - assert not errors - - -@pytest.mark.parametrize( - "config", - [ - {"commands": [{"name": "a"}, {"name": "a"}]}, - {"commands": [{"name": "a"}], "workflows": {"a": []}}, - {"commands": [{"name": "a"}], "workflows": {"b": ["c"]}}, - ], -) -def test_project_config_validation1(config): - with pytest.raises(SystemExit): - validate_project_commands(config) - - -@pytest.mark.parametrize( - "config,n_errors", - [ - ({"commands": {"a": []}}, 1), - ({"commands": [{"help": "..."}]}, 1), - ({"commands": [{"name": "a", "extra": "b"}]}, 1), - ({"commands": [{"extra": "b"}]}, 2), - ({"commands": [{"name": "a", "deps": [123]}]}, 1), - ], -) -def test_project_config_validation2(config, n_errors): - errors = validate(ProjectConfigSchema, config) - assert len(errors) == n_errors - - -@pytest.mark.parametrize( - "int_value", - [10, pytest.param("10", marks=pytest.mark.xfail)], -) -def test_project_config_interpolation(int_value): - variables = {"a": int_value, "b": {"c": "foo", "d": True}} - commands = [ - {"name": "x", "script": ["hello ${vars.a} ${vars.b.c}"]}, - {"name": "y", "script": ["${vars.b.c} ${vars.b.d}"]}, - ] - project = {"commands": commands, "vars": variables} - with make_tempdir() as d: - srsly.write_yaml(d / "project.yml", project) - cfg = load_project_config(d) - assert type(cfg) == dict - assert type(cfg["commands"]) == list - assert cfg["commands"][0]["script"][0] == "hello 10 foo" - assert cfg["commands"][1]["script"][0] == "foo true" - commands = [{"name": "x", "script": ["hello ${vars.a} ${vars.b.e}"]}] - project = {"commands": commands, "vars": variables} - with pytest.raises(ConfigValidationError): - substitute_project_variables(project) - - -@pytest.mark.parametrize( - "greeting", - [342, "everyone", "tout le monde", pytest.param("42", marks=pytest.mark.xfail)], -) -def test_project_config_interpolation_override(greeting): - variables = {"a": "world"} - commands = [ - {"name": "x", "script": ["hello ${vars.a}"]}, - ] - overrides = {"vars.a": greeting} - project = {"commands": commands, "vars": variables} - with make_tempdir() as d: - srsly.write_yaml(d / "project.yml", project) - cfg = load_project_config(d, overrides=overrides) - assert type(cfg) == dict - assert type(cfg["commands"]) == list - assert cfg["commands"][0]["script"][0] == f"hello {greeting}" - - -def test_project_config_interpolation_env(): - variables = {"a": 10} - env_var = "SPACY_TEST_FOO" - env_vars = {"foo": env_var} - commands = [{"name": "x", "script": ["hello ${vars.a} ${env.foo}"]}] - project = {"commands": commands, "vars": variables, "env": env_vars} - with make_tempdir() as d: - srsly.write_yaml(d / "project.yml", project) - cfg = load_project_config(d) - assert cfg["commands"][0]["script"][0] == "hello 10 " - os.environ[env_var] = "123" - with make_tempdir() as d: - srsly.write_yaml(d / "project.yml", project) - cfg = load_project_config(d) - assert cfg["commands"][0]["script"][0] == "hello 10 123" - - @pytest.mark.parametrize( "args,expected", [ @@ -784,21 +625,6 @@ def test_get_third_party_dependencies(): get_third_party_dependencies(nlp.config) -@pytest.mark.parametrize( - "parent,child,expected", - [ - ("/tmp", "/tmp", True), - ("/tmp", "/", False), - ("/tmp", "/tmp/subdir", True), - ("/tmp", "/tmpdir", False), - ("/tmp", "/tmp/subdir/..", True), - ("/tmp", "/tmp/..", False), - ], -) -def test_is_subpath_of(parent, child, expected): - assert is_subpath_of(parent, child) == expected - - @pytest.mark.slow @pytest.mark.parametrize( "factory_name,pipe_name", @@ -1044,60 +870,6 @@ def test_applycli_user_data(): assert result[0]._.ext == val -def test_local_remote_storage(): - with make_tempdir() as d: - filename = "a.txt" - - content_hashes = ("aaaa", "cccc", "bbbb") - for i, content_hash in enumerate(content_hashes): - # make sure that each subsequent file has a later timestamp - if i > 0: - time.sleep(1) - content = f"{content_hash} content" - loc_file = d / "root" / filename - if not loc_file.parent.exists(): - loc_file.parent.mkdir(parents=True) - with loc_file.open(mode="w") as file_: - file_.write(content) - - # push first version to remote storage - remote = RemoteStorage(d / "root", str(d / "remote")) - remote.push(filename, "aaaa", content_hash) - - # retrieve with full hashes - loc_file.unlink() - remote.pull(filename, command_hash="aaaa", content_hash=content_hash) - with loc_file.open(mode="r") as file_: - assert file_.read() == content - - # retrieve with command hash - loc_file.unlink() - remote.pull(filename, command_hash="aaaa") - with loc_file.open(mode="r") as file_: - assert file_.read() == content - - # retrieve with content hash - loc_file.unlink() - remote.pull(filename, content_hash=content_hash) - with loc_file.open(mode="r") as file_: - assert file_.read() == content - - # retrieve with no hashes - loc_file.unlink() - remote.pull(filename) - with loc_file.open(mode="r") as file_: - assert file_.read() == content - - -def test_local_remote_storage_pull_missing(): - # pulling from a non-existent remote pulls nothing gracefully - with make_tempdir() as d: - filename = "a.txt" - remote = RemoteStorage(d / "root", str(d / "remote")) - assert remote.pull(filename, command_hash="aaaa") is None - assert remote.pull(filename) is None - - def test_cli_find_threshold(capsys): def make_examples(nlp: Language) -> List[Example]: docs: List[Example] = [] @@ -1208,63 +980,6 @@ def test_cli_find_threshold(capsys): ) -@pytest.mark.filterwarnings("ignore::DeprecationWarning") -@pytest.mark.parametrize( - "reqs,output", - [ - [ - """ - spacy - - # comment - - thinc""", - (False, False), - ], - [ - """# comment - --some-flag - spacy""", - (False, False), - ], - [ - """# comment - --some-flag - spacy; python_version >= '3.6'""", - (False, False), - ], - [ - """# comment - spacyunknowndoesnotexist12345""", - (True, False), - ], - ], -) -def test_project_check_requirements(reqs, output): - import pkg_resources - - # excessive guard against unlikely package name - try: - pkg_resources.require("spacyunknowndoesnotexist12345") - except pkg_resources.DistributionNotFound: - assert output == _check_requirements([req.strip() for req in reqs.split("\n")]) - - -def test_upload_download_local_file(): - with make_tempdir() as d1, make_tempdir() as d2: - filename = "f.txt" - content = "content" - local_file = d1 / filename - remote_file = d2 / filename - with local_file.open(mode="w") as file_: - file_.write(content) - upload_file(local_file, remote_file) - local_file.unlink() - download_file(remote_file, local_file) - with local_file.open(mode="r") as file_: - assert file_.read() == content - - def test_walk_directory(): with make_tempdir() as d: files = [ diff --git a/spacy/util.py b/spacy/util.py index 762699a97..a2a033cbc 100644 --- a/spacy/util.py +++ b/spacy/util.py @@ -101,7 +101,6 @@ logger.addHandler(logger_stream_handler) class ENV_VARS: CONFIG_OVERRIDES = "SPACY_CONFIG_OVERRIDES" - PROJECT_USE_GIT_VERSION = "SPACY_PROJECT_USE_GIT_VERSION" class registry(thinc.registry): @@ -974,23 +973,12 @@ def replace_model_node(model: Model, target: Model, replacement: Model) -> None: def split_command(command: str) -> List[str]: """Split a string command using shlex. Handles platform compatibility. - command (str) : The command to split RETURNS (List[str]): The split command. """ return shlex.split(command, posix=not is_windows) -def join_command(command: List[str]) -> str: - """Join a command using shlex. shlex.join is only available for Python 3.8+, - so we're using a workaround here. - - command (List[str]): The command to join. - RETURNS (str): The joined command - """ - return " ".join(shlex.quote(cmd) for cmd in command) - - def run_command( command: Union[str, List[str]], *, @@ -999,7 +987,6 @@ def run_command( ) -> subprocess.CompletedProcess: """Run a command on the command line as a subprocess. If the subprocess returns a non-zero exit code, a system exit is performed. - command (str / List[str]): The command. If provided as a string, the string will be split using shlex.split. stdin (Optional[Any]): stdin to read from or None. @@ -1050,7 +1037,6 @@ def run_command( @contextmanager def working_dir(path: Union[str, Path]) -> Iterator[Path]: """Change current working directory and returns to previous on exit. - path (str / Path): The directory to navigate to. YIELDS (Path): The absolute path to the current working directory. This should be used if the block needs to perform actions within the working @@ -1069,7 +1055,6 @@ def working_dir(path: Union[str, Path]) -> Iterator[Path]: def make_tempdir() -> Generator[Path, None, None]: """Execute a block in a temporary directory and remove the directory and its contents at the end of the with block. - YIELDS (Path): The path of the temp directory. """ d = Path(tempfile.mkdtemp()) @@ -1087,15 +1072,6 @@ def make_tempdir() -> Generator[Path, None, None]: warnings.warn(Warnings.W091.format(dir=d, msg=e)) -def is_cwd(path: Union[Path, str]) -> bool: - """Check whether a path is the current working directory. - - path (Union[Path, str]): The directory path. - RETURNS (bool): Whether the path is the current working directory. - """ - return str(Path(path).resolve()).lower() == str(Path.cwd().resolve()).lower() - - def is_in_jupyter() -> bool: """Check if user is running spaCy from a Jupyter notebook by detecting the IPython kernel. Mainly used for the displaCy visualizer. From 41dba5bd344c2442906c7d3b74ad84e72b4a3847 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 7 Jul 2023 10:17:41 +0200 Subject: [PATCH 05/74] Update max_length default in span finder docs (#12803) --- website/docs/api/spanfinder.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/api/spanfinder.mdx b/website/docs/api/spanfinder.mdx index ca3104c85..ef4a6baa5 100644 --- a/website/docs/api/spanfinder.mdx +++ b/website/docs/api/spanfinder.mdx @@ -60,7 +60,7 @@ architectures and their arguments and hyperparameters. | `model` | A model instance that is given a list of documents and predicts a probability for each token. ~~Model[List[Doc], Floats2d]~~ | | `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ | | `threshold` | Minimum probability to consider a prediction positive. Defaults to `0.5`. ~~float~~ | -| `max_length` | Maximum length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ | +| `max_length` | Maximum length of the produced spans, defaults to `25`. ~~Optional[int]~~ | | `min_length` | Minimum length of the produced spans, defaults to `None` meaning shortest span length is 1. ~~Optional[int]~~ | | `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ | From 1a55661cfbb51d2dcbe2dbf725ea7c56aca80d7e Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 7 Jul 2023 10:52:33 +0200 Subject: [PATCH 06/74] Update website binder version to v3.6 (#12805) --- website/meta/site.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/meta/site.json b/website/meta/site.json index 3d4f2d5ee..08fcde62e 100644 --- a/website/meta/site.json +++ b/website/meta/site.json @@ -27,7 +27,7 @@ "indexName": "spacy" }, "binderUrl": "explosion/spacy-io-binder", - "binderVersion": "3.5", + "binderVersion": "3.6", "sections": [ { "id": "usage", "title": "Usage Documentation", "theme": "blue" }, { "id": "models", "title": "Models Documentation", "theme": "blue" }, From ddffd096024004f27a0dee3701dc248c4647b3a7 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Fri, 7 Jul 2023 15:18:16 +0200 Subject: [PATCH 07/74] Trainable lemmatizer docs link (#12795) * add an anchor to the trainable lemmatizer section * add requirement for morphologizer,tagger to rule-based lemmatizer * morphologizer only --- website/docs/usage/linguistic-features.mdx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/usage/linguistic-features.mdx b/website/docs/usage/linguistic-features.mdx index 55d5680fe..90f305ada 100644 --- a/website/docs/usage/linguistic-features.mdx +++ b/website/docs/usage/linguistic-features.mdx @@ -113,7 +113,7 @@ print(doc[2].morph) # 'Case=Nom|Person=2|PronType=Prs' print(doc[2].pos_) # 'PRON' ``` -## Lemmatization {id="lemmatization",model="lemmatizer",version="3"} +## Lemmatization {id="lemmatization",version="3"} spaCy provides two pipeline components for lemmatization: @@ -170,7 +170,7 @@ nlp = spacy.blank("sv") nlp.add_pipe("lemmatizer", config={"mode": "lookup"}) ``` -### Rule-based lemmatizer {id="lemmatizer-rule"} +### Rule-based lemmatizer {id="lemmatizer-rule",model="morphologizer"} When training pipelines that include a component that assigns part-of-speech tags (a morphologizer or a tagger with a [POS mapping](#mappings-exceptions)), a @@ -194,7 +194,7 @@ information, without consulting the context of the token. The rule-based lemmatizer also accepts list-based exception files. For English, these are acquired from [WordNet](https://wordnet.princeton.edu/). -### Trainable lemmatizer +### Trainable lemmatizer {id="lemmatizer-train",model="trainable_lemmatizer"} The [`EditTreeLemmatizer`](/api/edittreelemmatizer) can learn form-to-lemma transformations from a training corpus that includes lemma annotations. This From 0566c3a166c7ccfb5a1bddb025dddf9c576a9ed2 Mon Sep 17 00:00:00 2001 From: Connor Brinton Date: Thu, 13 Jul 2023 11:33:05 -0400 Subject: [PATCH 08/74] =?UTF-8?q?=F0=9F=90=9B=20Escape=20annotated=20HTML?= =?UTF-8?q?=20tags=20in=20span=20renderer=20(#12817)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit These changes add a missing call to `escape_html` in the displaCy span renderer. Previously span-annotated tokens would be inserted into the page markup without being escaped, resulting in potentially incorrect rendering. When I encountered this issue, it resulted in some docs and span underlines being superimposed on top of properly rendered docs and span underlines near the beginning of the visualization (due to an unescaped `` tag). --- spacy/displacy/render.py | 3 +-- spacy/tests/test_displacy.py | 19 +++++++++++++++++++ 2 files changed, 20 insertions(+), 2 deletions(-) diff --git a/spacy/displacy/render.py b/spacy/displacy/render.py index 86869e3b8..47407bcb7 100644 --- a/spacy/displacy/render.py +++ b/spacy/displacy/render.py @@ -1,4 +1,3 @@ -import itertools import uuid from typing import Any, Dict, List, Optional, Tuple, Union @@ -218,7 +217,7 @@ class SpanRenderer: + (self.offset_step * (len(entities) - 1)) ) markup += self.span_template.format( - text=token["text"], + text=escape_html(token["text"]), span_slices=slices, span_starts=starts, total_height=total_height, diff --git a/spacy/tests/test_displacy.py b/spacy/tests/test_displacy.py index ce103068a..1570f8d09 100644 --- a/spacy/tests/test_displacy.py +++ b/spacy/tests/test_displacy.py @@ -377,3 +377,22 @@ def test_displacy_manual_sorted_entities(): html = displacy.render(doc, style="ent", manual=True) assert html.find("FIRST") < html.find("SECOND") + + +@pytest.mark.issue(12816) +def test_issue12816(en_vocab) -> None: + """Test that displaCy's span visualizer escapes annotated HTML tags correctly.""" + # Create a doc containing an annotated word and an unannotated HTML tag + doc = Doc(en_vocab, words=["test", ""]) + doc.spans["sc"] = [Span(doc, 0, 1, label="test")] + + # Verify that the HTML tag is escaped when unannotated + html = displacy.render(doc, style="span") + assert "<TEST>" in html + + # Annotate the HTML tag + doc.spans["sc"].append(Span(doc, 1, 2, label="test")) + + # Verify that the HTML tag is still escaped + html = displacy.render(doc, style="span") + assert "<TEST>" in html From ef20e114e0c4984701e6bcd8af8b5cc5a12bc00a Mon Sep 17 00:00:00 2001 From: Ian Thompson Date: Fri, 14 Jul 2023 02:45:54 -0500 Subject: [PATCH 09/74] Typo fix in `Language.replace_listeners` docs (#12823) * modified: spacy/language.py - corrected typo in docstring for :method:`Language.replace_listeners` - added noqa comment on unused local variable assignment in :method:`Language.from_config` as I wasn't sure if it should be unassigned modified: website/docs/api/language.mdx - corrected typo in `Language.replace_listeners` markdown * modified: spacy/language.py - removed noqa comment --------- Co-authored-by: Ian Thompson --- spacy/language.py | 2 +- website/docs/api/language.mdx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/spacy/language.py b/spacy/language.py index fd616483b..3b3e33991 100644 --- a/spacy/language.py +++ b/spacy/language.py @@ -1958,7 +1958,7 @@ class Language: useful when training a pipeline with components sourced from an existing pipeline: if multiple components (e.g. tagger, parser, NER) listen to the same tok2vec component, but some of them are frozen and not updated, - their performance may degrade significally as the tok2vec component is + their performance may degrade significantly as the tok2vec component is updated with new data. To prevent this, listeners can be replaced with a standalone tok2vec layer that is owned by the component and doesn't change if the component isn't updated. diff --git a/website/docs/api/language.mdx b/website/docs/api/language.mdx index de23156b9..068e8ea78 100644 --- a/website/docs/api/language.mdx +++ b/website/docs/api/language.mdx @@ -856,7 +856,7 @@ token-to-vector embedding component like [`Tok2Vec`](/api/tok2vec) or training a pipeline with components sourced from an existing pipeline: if multiple components (e.g. tagger, parser, NER) listen to the same token-to-vector component, but some of them are frozen and not updated, their -performance may degrade significally as the token-to-vector component is updated +performance may degrade significantly as the token-to-vector component is updated with new data. To prevent this, listeners can be replaced with a standalone token-to-vector layer that is owned by the component and doesn't change if the component isn't updated. From 95075298f5e6b28ecf41faef83af4bc247e12ed7 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 18 Jul 2023 09:29:04 +0200 Subject: [PATCH 10/74] Update pex Makefile defaults (#12832) * Update pex Makefile defaults - switch to python 3.8 - only install spacy-lookups-data for extra packages * Update website for pex defaults --- Makefile | 4 ++-- website/docs/usage/index.mdx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 4de628663..c8f68be7f 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,11 @@ SHELL := /bin/bash ifndef SPACY_EXTRAS -override SPACY_EXTRAS = spacy-lookups-data==1.0.2 jieba spacy-pkuseg==0.0.28 sudachipy sudachidict_core pymorphy2 +override SPACY_EXTRAS = spacy-lookups-data==1.0.3 endif ifndef PYVER -override PYVER = 3.6 +override PYVER = 3.8 endif VENV := ./env$(PYVER) diff --git a/website/docs/usage/index.mdx b/website/docs/usage/index.mdx index 4b06178d5..414968d42 100644 --- a/website/docs/usage/index.mdx +++ b/website/docs/usage/index.mdx @@ -261,7 +261,7 @@ source code and recompiling frequently. #### Visual Studio Code extension -![spaCy extension demo](/images/spacy-extension-demo.gif) +![spaCy extension demo](/images/spacy-extension-demo.gif) The [spaCy VSCode Extension](https://github.com/explosion/spacy-vscode) provides additional tooling and features for working with spaCy's config files. Version @@ -310,7 +310,7 @@ You can configure the build process with the following environment variables: | Variable | Description | | -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `SPACY_EXTRAS` | Additional Python packages to install alongside spaCy with optional version specifications. Should be a string that can be passed to `pip install`. See [`Makefile`](%%GITHUB_SPACY/Makefile) for defaults. | -| `PYVER` | The Python version to build against. This version needs to be available on your build and runtime machines. Defaults to `3.6`. | +| `PYVER` | The Python version to build against. This version needs to be available on your build and runtime machines. Defaults to `3.8`. | | `WHEELHOUSE` | Directory to store the wheel files during compilation. Defaults to `./wheelhouse`. | ### Run tests {id="run-tests"} From 6bf7c65329a59055fa98e5a5493a4380397627b9 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 18 Jul 2023 10:00:07 +0200 Subject: [PATCH 11/74] Update matcher pattern validation tests (#12835) - parametrize over individual token patterns (as originally intended, as far as I can tell) - add a test for lowercase `in` in patterns --- spacy/tests/matcher/test_pattern_validation.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/spacy/tests/matcher/test_pattern_validation.py b/spacy/tests/matcher/test_pattern_validation.py index 21fa36865..45f9f4ee7 100644 --- a/spacy/tests/matcher/test_pattern_validation.py +++ b/spacy/tests/matcher/test_pattern_validation.py @@ -52,7 +52,8 @@ TEST_PATTERNS = [ @pytest.mark.parametrize( - "pattern", [[{"XX": "y"}, {"LENGTH": "2"}, {"TEXT": {"IN": 5}}]] + "pattern", + [[{"XX": "y"}], [{"LENGTH": "2"}], [{"TEXT": {"IN": 5}}], [{"text": {"in": 6}}]], ) def test_matcher_pattern_validation(en_vocab, pattern): matcher = Matcher(en_vocab, validate=True) From 1509c9669483abcd1b6c018cde5bc189dd04250b Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 18 Jul 2023 14:10:30 +0200 Subject: [PATCH 12/74] Clean up unused code in Language (#12836) Follow-up to #12701. --- spacy/language.py | 1 - 1 file changed, 1 deletion(-) diff --git a/spacy/language.py b/spacy/language.py index 3b3e33991..46f4a7996 100644 --- a/spacy/language.py +++ b/spacy/language.py @@ -1825,7 +1825,6 @@ class Language: # Later we replace the component config with the raw config again. interpolated = filled.interpolate() if not filled.is_interpolated else filled pipeline = interpolated.get("components", {}) - sourced = util.get_sourced_components(interpolated) # If components are loaded from a source (existing models), we cache # them here so they're only loaded once source_nlps = {} From b0228d8ea6f7cce9583c747fb84d9be8ae9a753e Mon Sep 17 00:00:00 2001 From: Basile Dura Date: Wed, 19 Jul 2023 12:03:31 +0200 Subject: [PATCH 13/74] ci: add cython linter (#12694) * chore: add cython-linter dev dependency * fix: lexeme.pyx * fix: morphology.pxd * fix: tokenizer.pxd * fix: vocab.pxd * fix: morphology.pxd (line length) * ci: add cython-lint * ci: fix cython-lint call * Fix kb/candidate.pyx. * Fix kb/kb.pyx. * Fix kb/kb_in_memory.pyx. * Fix kb. * Fix training/ partially. * Fix training/. Ignore trailing whitespaces and too long lines. * Fix ml/. * Fix matcher/. * Fix pipeline/. * Fix tokens/. * Fix build errors. Fix vocab.pyx. * Fix cython-lint install and run. * Fix lexeme.pyx, parts_of_speech.pxd, vectors.pyx. Temporarily disable cython-lint execution. * Fix attrs.pyx, lexeme.pyx, symbols.pxd, isort issues. * Make cython-lint install conditional. Fix tokenizer.pyx. * Fix remaining files. Reenable cython-lint check. * Readded parentheses. * Fix test_build_dependencies(). * Add explanatory comment to cython-lint execution. --------- Co-authored-by: Raphael Mitsch --- .github/workflows/tests.yml | 6 + requirements.txt | 1 + spacy/attrs.pxd | 2 +- spacy/attrs.pyx | 2 +- spacy/kb/candidate.pxd | 3 +- spacy/kb/candidate.pyx | 27 +- spacy/kb/kb.pyx | 49 +++- spacy/kb/kb_in_memory.pxd | 62 +++-- spacy/kb/kb_in_memory.pyx | 154 +++++++---- spacy/lexeme.pyx | 17 +- spacy/matcher/dependencymatcher.pyx | 10 +- spacy/matcher/matcher.pyx | 246 +++++++++++------- spacy/matcher/phrasematcher.pyx | 4 +- spacy/ml/parser_model.pxd | 17 +- spacy/ml/parser_model.pyx | 129 +++++---- spacy/morphology.pxd | 8 +- spacy/morphology.pyx | 10 +- spacy/parts_of_speech.pxd | 2 +- .../_edit_tree_internals/edit_trees.pxd | 17 +- .../_edit_tree_internals/edit_trees.pyx | 14 +- .../_parser_internals/_beam_utils.pyx | 6 +- spacy/pipeline/_parser_internals/_state.pxd | 1 - .../pipeline/_parser_internals/arc_eager.pyx | 18 +- spacy/pipeline/_parser_internals/ner.pyx | 17 +- spacy/pipeline/_parser_internals/nonproj.pyx | 12 +- .../pipeline/_parser_internals/stateclass.pyx | 24 +- .../_parser_internals/transition_system.pxd | 14 +- .../_parser_internals/transition_system.pyx | 3 - spacy/pipeline/dep_parser.pyx | 3 +- spacy/pipeline/morphologizer.pyx | 11 +- spacy/pipeline/multitask.pyx | 9 +- spacy/pipeline/ner.pyx | 5 +- spacy/pipeline/pipe.pyx | 6 +- spacy/pipeline/sentencizer.pyx | 28 +- spacy/pipeline/senter.pyx | 1 - spacy/pipeline/tagger.pyx | 11 +- spacy/pipeline/trainable_pipe.pyx | 14 +- spacy/pipeline/transition_parser.pxd | 18 +- spacy/pipeline/transition_parser.pyx | 64 ++--- spacy/strings.pyx | 5 +- spacy/structs.pxd | 2 +- spacy/symbols.pxd | 8 +- spacy/symbols.pyx | 8 +- spacy/tests/package/test_requirements.py | 1 + spacy/tokenizer.pxd | 76 ++++-- spacy/tokenizer.pyx | 37 ++- spacy/tokens/_retokenize.pyx | 27 +- spacy/tokens/doc.pxd | 3 +- spacy/tokens/doc.pyx | 32 +-- spacy/tokens/graph.pyx | 53 ++-- spacy/tokens/morphanalysis.pyx | 1 - spacy/tokens/span.pyx | 9 +- spacy/tokens/span_group.pyx | 6 +- spacy/tokens/token.pxd | 4 +- spacy/tokens/token.pyx | 8 +- spacy/training/align.pyx | 12 +- spacy/training/example.pyx | 15 +- spacy/training/gold_io.pyx | 31 ++- spacy/vectors.pyx | 32 +-- spacy/vocab.pxd | 2 +- spacy/vocab.pyx | 23 +- 61 files changed, 846 insertions(+), 594 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d60c90c1c..4099b31e2 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -45,6 +45,12 @@ jobs: run: | python -m pip install flake8==5.0.4 python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics + - name: cython-lint + run: | + python -m pip install cython-lint -c requirements.txt + # E501: line too log, W291: trailing whitespace, E266: too many leading '#' for block comment + cython-lint spacy --ignore E501,W291,E266 + tests: name: Test needs: Validate diff --git a/requirements.txt b/requirements.txt index a007f495e..4a131d18c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -38,4 +38,5 @@ types-setuptools>=57.0.0 types-requests types-setuptools>=57.0.0 black==22.3.0 +cython-lint>=0.15.0; python_version >= "3.7" isort>=5.0,<6.0 diff --git a/spacy/attrs.pxd b/spacy/attrs.pxd index 6dc9ecaee..fbbac0ec2 100644 --- a/spacy/attrs.pxd +++ b/spacy/attrs.pxd @@ -96,4 +96,4 @@ cdef enum attr_id_t: ENT_ID = symbols.ENT_ID IDX - SENT_END \ No newline at end of file + SENT_END diff --git a/spacy/attrs.pyx b/spacy/attrs.pyx index dc8eed7c3..97b5d5e36 100644 --- a/spacy/attrs.pyx +++ b/spacy/attrs.pyx @@ -117,7 +117,7 @@ def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False): if "pos" in stringy_attrs: stringy_attrs["TAG"] = stringy_attrs.pop("pos") if "morph" in stringy_attrs: - morphs = stringy_attrs.pop("morph") + morphs = stringy_attrs.pop("morph") # no-cython-lint if "number" in stringy_attrs: stringy_attrs.pop("number") if "tenspect" in stringy_attrs: diff --git a/spacy/kb/candidate.pxd b/spacy/kb/candidate.pxd index 9fc4c4e9d..80fcbc459 100644 --- a/spacy/kb/candidate.pxd +++ b/spacy/kb/candidate.pxd @@ -4,7 +4,8 @@ from ..typedefs cimport hash_t from .kb cimport KnowledgeBase -# Object used by the Entity Linker that summarizes one entity-alias candidate combination. +# Object used by the Entity Linker that summarizes one entity-alias candidate +# combination. cdef class Candidate: cdef readonly KnowledgeBase kb cdef hash_t entity_hash diff --git a/spacy/kb/candidate.pyx b/spacy/kb/candidate.pyx index 4cd734f43..53fc9b036 100644 --- a/spacy/kb/candidate.pyx +++ b/spacy/kb/candidate.pyx @@ -8,15 +8,24 @@ from ..tokens import Span cdef class Candidate: - """A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved - to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking - algorithm which will disambiguate the various candidates to the correct one. + """A `Candidate` object refers to a textual mention (`alias`) that may or + may not be resolved to a specific `entity` from a Knowledge Base. This + will be used as input for the entity linking algorithm which will + disambiguate the various candidates to the correct one. Each candidate (alias, entity) pair is assigned a certain prior probability. DOCS: https://spacy.io/api/kb/#candidate-init """ - def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob): + def __init__( + self, + KnowledgeBase kb, + entity_hash, + entity_freq, + entity_vector, + alias_hash, + prior_prob + ): self.kb = kb self.entity_hash = entity_hash self.entity_freq = entity_freq @@ -59,7 +68,8 @@ cdef class Candidate: def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]: """ - Return candidate entities for a given mention and fetching appropriate entries from the index. + Return candidate entities for a given mention and fetching appropriate + entries from the index. kb (KnowledgeBase): Knowledge base to query. mention (Span): Entity mention for which to identify candidates. RETURNS (Iterable[Candidate]): Identified candidates. @@ -67,9 +77,12 @@ def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]: return kb.get_candidates(mention) -def get_candidates_batch(kb: KnowledgeBase, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]: +def get_candidates_batch( + kb: KnowledgeBase, mentions: Iterable[Span] +) -> Iterable[Iterable[Candidate]]: """ - Return candidate entities for the given mentions and fetching appropriate entries from the index. + Return candidate entities for the given mentions and fetching appropriate entries + from the index. kb (KnowledgeBase): Knowledge base to query. mention (Iterable[Span]): Entity mentions for which to identify candidates. RETURNS (Iterable[Iterable[Candidate]]): Identified candidates. diff --git a/spacy/kb/kb.pyx b/spacy/kb/kb.pyx index a88e18e1f..6ad4c3564 100644 --- a/spacy/kb/kb.pyx +++ b/spacy/kb/kb.pyx @@ -12,8 +12,9 @@ from .candidate import Candidate cdef class KnowledgeBase: - """A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases, - to support entity linking of named entities to real-world concepts. + """A `KnowledgeBase` instance stores unique identifiers for entities and + their textual aliases, to support entity linking of named entities to + real-world concepts. This is an abstract class and requires its operations to be implemented. DOCS: https://spacy.io/api/kb @@ -31,10 +32,13 @@ cdef class KnowledgeBase: self.entity_vector_length = entity_vector_length self.mem = Pool() - def get_candidates_batch(self, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]: + def get_candidates_batch( + self, mentions: Iterable[Span] + ) -> Iterable[Iterable[Candidate]]: """ - Return candidate entities for specified texts. Each candidate defines the entity, the original alias, - and the prior probability of that alias resolving to that entity. + Return candidate entities for specified texts. Each candidate defines + the entity, the original alias, and the prior probability of that + alias resolving to that entity. If no candidate is found for a given text, an empty list is returned. mentions (Iterable[Span]): Mentions for which to get candidates. RETURNS (Iterable[Iterable[Candidate]]): Identified candidates. @@ -43,14 +47,17 @@ cdef class KnowledgeBase: def get_candidates(self, mention: Span) -> Iterable[Candidate]: """ - Return candidate entities for specified text. Each candidate defines the entity, the original alias, + Return candidate entities for specified text. Each candidate defines + the entity, the original alias, and the prior probability of that alias resolving to that entity. If the no candidate is found for a given text, an empty list is returned. mention (Span): Mention for which to get candidates. RETURNS (Iterable[Candidate]): Identified candidates. """ raise NotImplementedError( - Errors.E1045.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__) + Errors.E1045.format( + parent="KnowledgeBase", method="get_candidates", name=self.__name__ + ) ) def get_vectors(self, entities: Iterable[str]) -> Iterable[Iterable[float]]: @@ -68,7 +75,9 @@ cdef class KnowledgeBase: RETURNS (Iterable[float]): Vector for specified entity. """ raise NotImplementedError( - Errors.E1045.format(parent="KnowledgeBase", method="get_vector", name=self.__name__) + Errors.E1045.format( + parent="KnowledgeBase", method="get_vector", name=self.__name__ + ) ) def to_bytes(self, **kwargs) -> bytes: @@ -76,7 +85,9 @@ cdef class KnowledgeBase: RETURNS (bytes): Current state as binary string. """ raise NotImplementedError( - Errors.E1045.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__) + Errors.E1045.format( + parent="KnowledgeBase", method="to_bytes", name=self.__name__ + ) ) def from_bytes(self, bytes_data: bytes, *, exclude: Tuple[str] = tuple()): @@ -85,25 +96,35 @@ cdef class KnowledgeBase: exclude (Tuple[str]): Properties to exclude when restoring KB. """ raise NotImplementedError( - Errors.E1045.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__) + Errors.E1045.format( + parent="KnowledgeBase", method="from_bytes", name=self.__name__ + ) ) - def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None: + def to_disk( + self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList() + ) -> None: """ Write KnowledgeBase content to disk. path (Union[str, Path]): Target file path. exclude (Iterable[str]): List of components to exclude. """ raise NotImplementedError( - Errors.E1045.format(parent="KnowledgeBase", method="to_disk", name=self.__name__) + Errors.E1045.format( + parent="KnowledgeBase", method="to_disk", name=self.__name__ + ) ) - def from_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None: + def from_disk( + self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList() + ) -> None: """ Load KnowledgeBase content from disk. path (Union[str, Path]): Target file path. exclude (Iterable[str]): List of components to exclude. """ raise NotImplementedError( - Errors.E1045.format(parent="KnowledgeBase", method="from_disk", name=self.__name__) + Errors.E1045.format( + parent="KnowledgeBase", method="from_disk", name=self.__name__ + ) ) diff --git a/spacy/kb/kb_in_memory.pxd b/spacy/kb/kb_in_memory.pxd index 08ec6b2a3..e0e33301a 100644 --- a/spacy/kb/kb_in_memory.pxd +++ b/spacy/kb/kb_in_memory.pxd @@ -55,23 +55,28 @@ cdef class InMemoryLookupKB(KnowledgeBase): # optional data, we can let users configure a DB as the backend for this. cdef object _features_table - cdef inline int64_t c_add_vector(self, vector[float] entity_vector) nogil: """Add an entity vector to the vectors table.""" cdef int64_t new_index = self._vectors_table.size() self._vectors_table.push_back(entity_vector) return new_index - - cdef inline int64_t c_add_entity(self, hash_t entity_hash, float freq, - int32_t vector_index, int feats_row) nogil: + cdef inline int64_t c_add_entity( + self, + hash_t entity_hash, + float freq, + int32_t vector_index, + int feats_row + ) nogil: """Add an entry to the vector of entries. - After calling this method, make sure to update also the _entry_index using the return value""" + After calling this method, make sure to update also the _entry_index + using the return value""" # This is what we'll map the entity hash key to. It's where the entry will sit # in the vector of entries, so we can get it later. cdef int64_t new_index = self._entries.size() - # Avoid struct initializer to enable nogil, cf https://github.com/cython/cython/issues/1642 + # Avoid struct initializer to enable nogil, cf. + # https://github.com/cython/cython/issues/1642 cdef KBEntryC entry entry.entity_hash = entity_hash entry.vector_index = vector_index @@ -81,11 +86,17 @@ cdef class InMemoryLookupKB(KnowledgeBase): self._entries.push_back(entry) return new_index - cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs) nogil: - """Connect a mention to a list of potential entities with their prior probabilities . - After calling this method, make sure to update also the _alias_index using the return value""" - # This is what we'll map the alias hash key to. It's where the alias will be defined - # in the vector of aliases. + cdef inline int64_t c_add_aliases( + self, + hash_t alias_hash, + vector[int64_t] entry_indices, + vector[float] probs + ) nogil: + """Connect a mention to a list of potential entities with their prior + probabilities. After calling this method, make sure to update also the + _alias_index using the return value""" + # This is what we'll map the alias hash key to. It's where the alias will be + # defined in the vector of aliases. cdef int64_t new_index = self._aliases_table.size() # Avoid struct initializer to enable nogil @@ -98,8 +109,9 @@ cdef class InMemoryLookupKB(KnowledgeBase): cdef inline void _create_empty_vectors(self, hash_t dummy_hash) nogil: """ - Initializing the vectors and making sure the first element of each vector is a dummy, - because the PreshMap maps pointing to indices in these vectors can not contain 0 as value + Initializing the vectors and making sure the first element of each vector is a + dummy, because the PreshMap maps pointing to indices in these vectors can not + contain 0 as value. cf. https://github.com/explosion/preshed/issues/17 """ cdef int32_t dummy_value = 0 @@ -130,12 +142,18 @@ cdef class InMemoryLookupKB(KnowledgeBase): cdef class Writer: cdef FILE* _fp - cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1 + cdef int write_header( + self, int64_t nr_entries, int64_t entity_vector_length + ) except -1 cdef int write_vector_element(self, float element) except -1 - cdef int write_entry(self, hash_t entry_hash, float entry_freq, int32_t vector_index) except -1 + cdef int write_entry( + self, hash_t entry_hash, float entry_freq, int32_t vector_index + ) except -1 cdef int write_alias_length(self, int64_t alias_length) except -1 - cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1 + cdef int write_alias_header( + self, hash_t alias_hash, int64_t candidate_length + ) except -1 cdef int write_alias(self, int64_t entry_index, float prob) except -1 cdef int _write(self, void* value, size_t size) except -1 @@ -143,12 +161,18 @@ cdef class Writer: cdef class Reader: cdef FILE* _fp - cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1 + cdef int read_header( + self, int64_t* nr_entries, int64_t* entity_vector_length + ) except -1 cdef int read_vector_element(self, float* element) except -1 - cdef int read_entry(self, hash_t* entity_hash, float* freq, int32_t* vector_index) except -1 + cdef int read_entry( + self, hash_t* entity_hash, float* freq, int32_t* vector_index + ) except -1 cdef int read_alias_length(self, int64_t* alias_length) except -1 - cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1 + cdef int read_alias_header( + self, hash_t* alias_hash, int64_t* candidate_length + ) except -1 cdef int read_alias(self, int64_t* entry_index, float* prob) except -1 cdef int _read(self, void* value, size_t size) except -1 diff --git a/spacy/kb/kb_in_memory.pyx b/spacy/kb/kb_in_memory.pyx index e991f7720..02773cbae 100644 --- a/spacy/kb/kb_in_memory.pyx +++ b/spacy/kb/kb_in_memory.pyx @@ -1,5 +1,5 @@ # cython: infer_types=True, profile=True -from typing import Any, Callable, Dict, Iterable, Union +from typing import Any, Callable, Dict, Iterable import srsly @@ -27,8 +27,9 @@ from .candidate import Candidate as Candidate cdef class InMemoryLookupKB(KnowledgeBase): - """An `InMemoryLookupKB` instance stores unique identifiers for entities and their textual aliases, - to support entity linking of named entities to real-world concepts. + """An `InMemoryLookupKB` instance stores unique identifiers for entities + and their textual aliases, to support entity linking of named entities to + real-world concepts. DOCS: https://spacy.io/api/inmemorylookupkb """ @@ -71,7 +72,8 @@ cdef class InMemoryLookupKB(KnowledgeBase): def add_entity(self, str entity, float freq, vector[float] entity_vector): """ - Add an entity to the KB, optionally specifying its log probability based on corpus frequency + Add an entity to the KB, optionally specifying its log probability + based on corpus frequency. Return the hash of the entity ID/name at the end. """ cdef hash_t entity_hash = self.vocab.strings.add(entity) @@ -83,14 +85,20 @@ cdef class InMemoryLookupKB(KnowledgeBase): # Raise an error if the provided entity vector is not of the correct length if len(entity_vector) != self.entity_vector_length: - raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length)) + raise ValueError( + Errors.E141.format( + found=len(entity_vector), required=self.entity_vector_length + ) + ) vector_index = self.c_add_vector(entity_vector=entity_vector) - new_index = self.c_add_entity(entity_hash=entity_hash, - freq=freq, - vector_index=vector_index, - feats_row=-1) # Features table currently not implemented + new_index = self.c_add_entity( + entity_hash=entity_hash, + freq=freq, + vector_index=vector_index, + feats_row=-1 + ) # Features table currently not implemented self._entry_index[entity_hash] = new_index return entity_hash @@ -115,7 +123,12 @@ cdef class InMemoryLookupKB(KnowledgeBase): else: entity_vector = vector_list[i] if len(entity_vector) != self.entity_vector_length: - raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length)) + raise ValueError( + Errors.E141.format( + found=len(entity_vector), + required=self.entity_vector_length + ) + ) entry.entity_hash = entity_hash entry.freq = freq_list[i] @@ -149,11 +162,15 @@ cdef class InMemoryLookupKB(KnowledgeBase): previous_alias_nr = self.get_size_aliases() # Throw an error if the length of entities and probabilities are not the same if not len(entities) == len(probabilities): - raise ValueError(Errors.E132.format(alias=alias, - entities_length=len(entities), - probabilities_length=len(probabilities))) + raise ValueError( + Errors.E132.format( + alias=alias, + entities_length=len(entities), + probabilities_length=len(probabilities)) + ) - # Throw an error if the probabilities sum up to more than 1 (allow for some rounding errors) + # Throw an error if the probabilities sum up to more than 1 (allow for + # some rounding errors) prob_sum = sum(probabilities) if prob_sum > 1.00001: raise ValueError(Errors.E133.format(alias=alias, sum=prob_sum)) @@ -170,40 +187,47 @@ cdef class InMemoryLookupKB(KnowledgeBase): for entity, prob in zip(entities, probabilities): entity_hash = self.vocab.strings[entity] - if not entity_hash in self._entry_index: + if entity_hash not in self._entry_index: raise ValueError(Errors.E134.format(entity=entity)) entry_index = self._entry_index.get(entity_hash) entry_indices.push_back(int(entry_index)) probs.push_back(float(prob)) - new_index = self.c_add_aliases(alias_hash=alias_hash, entry_indices=entry_indices, probs=probs) + new_index = self.c_add_aliases( + alias_hash=alias_hash, entry_indices=entry_indices, probs=probs + ) self._alias_index[alias_hash] = new_index if previous_alias_nr + 1 != self.get_size_aliases(): raise RuntimeError(Errors.E891.format(alias=alias)) return alias_hash - def append_alias(self, str alias, str entity, float prior_prob, ignore_warnings=False): + def append_alias( + self, str alias, str entity, float prior_prob, ignore_warnings=False + ): """ - For an alias already existing in the KB, extend its potential entities with one more. + For an alias already existing in the KB, extend its potential entities + with one more. Throw a warning if either the alias or the entity is unknown, or when the combination is already previously recorded. Throw an error if this entity+prior prob would exceed the sum of 1. - For efficiency, it's best to use the method `add_alias` as much as possible instead of this one. + For efficiency, it's best to use the method `add_alias` as much as + possible instead of this one. """ # Check if the alias exists in the KB cdef hash_t alias_hash = self.vocab.strings[alias] - if not alias_hash in self._alias_index: + if alias_hash not in self._alias_index: raise ValueError(Errors.E176.format(alias=alias)) # Check if the entity exists in the KB cdef hash_t entity_hash = self.vocab.strings[entity] - if not entity_hash in self._entry_index: + if entity_hash not in self._entry_index: raise ValueError(Errors.E134.format(entity=entity)) entry_index = self._entry_index.get(entity_hash) - # Throw an error if the prior probabilities (including the new one) sum up to more than 1 + # Throw an error if the prior probabilities (including the new one) + # sum up to more than 1 alias_index = self._alias_index.get(alias_hash) alias_entry = self._aliases_table[alias_index] current_sum = sum([p for p in alias_entry.probs]) @@ -236,12 +260,13 @@ cdef class InMemoryLookupKB(KnowledgeBase): def get_alias_candidates(self, str alias) -> Iterable[Candidate]: """ - Return candidate entities for an alias. Each candidate defines the entity, the original alias, - and the prior probability of that alias resolving to that entity. + Return candidate entities for an alias. Each candidate defines the + entity, the original alias, and the prior probability of that alias + resolving to that entity. If the alias is not known in the KB, and empty list is returned. """ cdef hash_t alias_hash = self.vocab.strings[alias] - if not alias_hash in self._alias_index: + if alias_hash not in self._alias_index: return [] alias_index = self._alias_index.get(alias_hash) alias_entry = self._aliases_table[alias_index] @@ -249,10 +274,14 @@ cdef class InMemoryLookupKB(KnowledgeBase): return [Candidate(kb=self, entity_hash=self._entries[entry_index].entity_hash, entity_freq=self._entries[entry_index].freq, - entity_vector=self._vectors_table[self._entries[entry_index].vector_index], + entity_vector=self._vectors_table[ + self._entries[entry_index].vector_index + ], alias_hash=alias_hash, prior_prob=prior_prob) - for (entry_index, prior_prob) in zip(alias_entry.entry_indices, alias_entry.probs) + for (entry_index, prior_prob) in zip( + alias_entry.entry_indices, alias_entry.probs + ) if entry_index != 0] def get_vector(self, str entity): @@ -266,8 +295,9 @@ cdef class InMemoryLookupKB(KnowledgeBase): return self._vectors_table[self._entries[entry_index].vector_index] def get_prior_prob(self, str entity, str alias): - """ Return the prior probability of a given alias being linked to a given entity, - or return 0.0 when this combination is not known in the knowledge base""" + """ Return the prior probability of a given alias being linked to a + given entity, or return 0.0 when this combination is not known in the + knowledge base.""" cdef hash_t alias_hash = self.vocab.strings[alias] cdef hash_t entity_hash = self.vocab.strings[entity] @@ -278,7 +308,9 @@ cdef class InMemoryLookupKB(KnowledgeBase): entry_index = self._entry_index[entity_hash] alias_entry = self._aliases_table[alias_index] - for (entry_index, prior_prob) in zip(alias_entry.entry_indices, alias_entry.probs): + for (entry_index, prior_prob) in zip( + alias_entry.entry_indices, alias_entry.probs + ): if self._entries[entry_index].entity_hash == entity_hash: return prior_prob @@ -288,13 +320,19 @@ cdef class InMemoryLookupKB(KnowledgeBase): """Serialize the current state to a binary string. """ def serialize_header(): - header = (self.get_size_entities(), self.get_size_aliases(), self.entity_vector_length) + header = ( + self.get_size_entities(), + self.get_size_aliases(), + self.entity_vector_length + ) return srsly.json_dumps(header) def serialize_entries(): i = 1 tuples = [] - for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]): + for entry_hash, entry_index in sorted( + self._entry_index.items(), key=lambda x: x[1] + ): entry = self._entries[entry_index] assert entry.entity_hash == entry_hash assert entry_index == i @@ -307,7 +345,9 @@ cdef class InMemoryLookupKB(KnowledgeBase): headers = [] indices_lists = [] probs_lists = [] - for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]): + for alias_hash, alias_index in sorted( + self._alias_index.items(), key=lambda x: x[1] + ): alias = self._aliases_table[alias_index] assert alias_index == i candidate_length = len(alias.entry_indices) @@ -365,7 +405,7 @@ cdef class InMemoryLookupKB(KnowledgeBase): indices = srsly.json_loads(all_data[1]) probs = srsly.json_loads(all_data[2]) for header, indices, probs in zip(headers, indices, probs): - alias_hash, candidate_length = header + alias_hash, _candidate_length = header alias.entry_indices = indices alias.probs = probs self._aliases_table[i] = alias @@ -414,10 +454,14 @@ cdef class InMemoryLookupKB(KnowledgeBase): writer.write_vector_element(element) i = i+1 - # dumping the entry records in the order in which they are in the _entries vector. - # index 0 is a dummy object not stored in the _entry_index and can be ignored. + # dumping the entry records in the order in which they are in the + # _entries vector. + # index 0 is a dummy object not stored in the _entry_index and can + # be ignored. i = 1 - for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]): + for entry_hash, entry_index in sorted( + self._entry_index.items(), key=lambda x: x[1] + ): entry = self._entries[entry_index] assert entry.entity_hash == entry_hash assert entry_index == i @@ -429,7 +473,9 @@ cdef class InMemoryLookupKB(KnowledgeBase): # dumping the aliases in the order in which they are in the _alias_index vector. # index 0 is a dummy object not stored in the _aliases_table and can be ignored. i = 1 - for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]): + for alias_hash, alias_index in sorted( + self._alias_index.items(), key=lambda x: x[1] + ): alias = self._aliases_table[alias_index] assert alias_index == i @@ -535,7 +581,8 @@ cdef class Writer: def __init__(self, path): assert isinstance(path, Path) content = bytes(path) - cdef bytes bytes_loc = content.encode('utf8') if type(content) == str else content + cdef bytes bytes_loc = content.encode('utf8') \ + if type(content) == str else content self._fp = fopen(bytes_loc, 'wb') if not self._fp: raise IOError(Errors.E146.format(path=path)) @@ -545,14 +592,18 @@ cdef class Writer: cdef size_t status = fclose(self._fp) assert status == 0 - cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1: + cdef int write_header( + self, int64_t nr_entries, int64_t entity_vector_length + ) except -1: self._write(&nr_entries, sizeof(nr_entries)) self._write(&entity_vector_length, sizeof(entity_vector_length)) cdef int write_vector_element(self, float element) except -1: self._write(&element, sizeof(element)) - cdef int write_entry(self, hash_t entry_hash, float entry_freq, int32_t vector_index) except -1: + cdef int write_entry( + self, hash_t entry_hash, float entry_freq, int32_t vector_index + ) except -1: self._write(&entry_hash, sizeof(entry_hash)) self._write(&entry_freq, sizeof(entry_freq)) self._write(&vector_index, sizeof(vector_index)) @@ -561,7 +612,9 @@ cdef class Writer: cdef int write_alias_length(self, int64_t alias_length) except -1: self._write(&alias_length, sizeof(alias_length)) - cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1: + cdef int write_alias_header( + self, hash_t alias_hash, int64_t candidate_length + ) except -1: self._write(&alias_hash, sizeof(alias_hash)) self._write(&candidate_length, sizeof(candidate_length)) @@ -577,16 +630,19 @@ cdef class Writer: cdef class Reader: def __init__(self, path): content = bytes(path) - cdef bytes bytes_loc = content.encode('utf8') if type(content) == str else content + cdef bytes bytes_loc = content.encode('utf8') \ + if type(content) == str else content self._fp = fopen(bytes_loc, 'rb') if not self._fp: PyErr_SetFromErrno(IOError) - status = fseek(self._fp, 0, 0) # this can be 0 if there is no header + fseek(self._fp, 0, 0) # this can be 0 if there is no header def __dealloc__(self): fclose(self._fp) - cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1: + cdef int read_header( + self, int64_t* nr_entries, int64_t* entity_vector_length + ) except -1: status = self._read(nr_entries, sizeof(int64_t)) if status < 1: if feof(self._fp): @@ -606,7 +662,9 @@ cdef class Reader: return 0 # end of file raise IOError(Errors.E145.format(param="vector element")) - cdef int read_entry(self, hash_t* entity_hash, float* freq, int32_t* vector_index) except -1: + cdef int read_entry( + self, hash_t* entity_hash, float* freq, int32_t* vector_index + ) except -1: status = self._read(entity_hash, sizeof(hash_t)) if status < 1: if feof(self._fp): @@ -637,7 +695,9 @@ cdef class Reader: return 0 # end of file raise IOError(Errors.E145.format(param="alias length")) - cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1: + cdef int read_alias_header( + self, hash_t* alias_hash, int64_t* candidate_length + ) except -1: status = self._read(alias_hash, sizeof(hash_t)) if status < 1: if feof(self._fp): diff --git a/spacy/lexeme.pyx b/spacy/lexeme.pyx index 00e2c6258..60d22e615 100644 --- a/spacy/lexeme.pyx +++ b/spacy/lexeme.pyx @@ -1,7 +1,6 @@ # cython: embedsignature=True # Compiler crashes on memory view coercion without this. Should report bug. cimport numpy as np -from cython.view cimport array as cvarray from libc.string cimport memset np.import_array() @@ -35,7 +34,7 @@ from .typedefs cimport attr_t, flags_t from .attrs import intify_attrs from .errors import Errors, Warnings -OOV_RANK = 0xffffffffffffffff # UINT64_MAX +OOV_RANK = 0xffffffffffffffff # UINT64_MAX memset(&EMPTY_LEXEME, 0, sizeof(LexemeC)) EMPTY_LEXEME.id = OOV_RANK @@ -105,7 +104,7 @@ cdef class Lexeme: if isinstance(value, float): continue elif isinstance(value, (int, long)): - Lexeme.set_struct_attr(self.c, attr, value) + Lexeme.set_struct_attr(self.c, attr, value) else: Lexeme.set_struct_attr(self.c, attr, self.vocab.strings.add(value)) @@ -137,10 +136,12 @@ cdef class Lexeme: if hasattr(other, "orth"): if self.c.orth == other.orth: return 1.0 - elif hasattr(other, "__len__") and len(other) == 1 \ - and hasattr(other[0], "orth"): - if self.c.orth == other[0].orth: - return 1.0 + elif ( + hasattr(other, "__len__") and len(other) == 1 + and hasattr(other[0], "orth") + and self.c.orth == other[0].orth + ): + return 1.0 if self.vector_norm == 0 or other.vector_norm == 0: warnings.warn(Warnings.W008.format(obj="Lexeme")) return 0.0 @@ -149,7 +150,7 @@ cdef class Lexeme: result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm) # ensure we get a scalar back (numpy does this automatically but cupy doesn't) return result.item() - + @property def has_vector(self): """RETURNS (bool): Whether a word vector is associated with the object. diff --git a/spacy/matcher/dependencymatcher.pyx b/spacy/matcher/dependencymatcher.pyx index a214c0668..348e000ff 100644 --- a/spacy/matcher/dependencymatcher.pyx +++ b/spacy/matcher/dependencymatcher.pyx @@ -108,7 +108,7 @@ cdef class DependencyMatcher: key (str): The match ID. RETURNS (bool): Whether the matcher contains rules for this match ID. """ - return self.has_key(key) + return self.has_key(key) # no-cython-lint: W601 def _validate_input(self, pattern, key): idx = 0 @@ -264,7 +264,7 @@ cdef class DependencyMatcher: def remove(self, key): key = self._normalize_key(key) - if not key in self._patterns: + if key not in self._patterns: raise ValueError(Errors.E175.format(key=key)) self._patterns.pop(key) self._raw_patterns.pop(key) @@ -382,7 +382,7 @@ cdef class DependencyMatcher: return [] return [doc[node].head] - def _gov(self,doc,node): + def _gov(self, doc, node): return list(doc[node].children) def _dep_chain(self, doc, node): @@ -443,7 +443,7 @@ cdef class DependencyMatcher: def _right_child(self, doc, node): return [child for child in doc[node].rights] - + def _left_child(self, doc, node): return [child for child in doc[node].lefts] @@ -461,7 +461,7 @@ cdef class DependencyMatcher: if doc[node].head.i > node: return [doc[node].head] return [] - + def _left_parent(self, doc, node): if doc[node].head.i < node: return [doc[node].head] diff --git a/spacy/matcher/matcher.pyx b/spacy/matcher/matcher.pyx index 3d03f37ae..167f85af4 100644 --- a/spacy/matcher/matcher.pyx +++ b/spacy/matcher/matcher.pyx @@ -12,31 +12,18 @@ import warnings import srsly -from ..attrs cimport ( - DEP, - ENT_IOB, - ID, - LEMMA, - MORPH, - NULL_ATTR, - ORTH, - POS, - TAG, - attr_id_t, -) +from ..attrs cimport DEP, ENT_IOB, ID, LEMMA, MORPH, NULL_ATTR, POS, TAG from ..structs cimport TokenC from ..tokens.doc cimport Doc, get_token_attr_for_matcher from ..tokens.morphanalysis cimport MorphAnalysis from ..tokens.span cimport Span from ..tokens.token cimport Token from ..typedefs cimport attr_t -from ..vocab cimport Vocab from ..attrs import IDS from ..errors import Errors, MatchPatternError, Warnings from ..schemas import validate_token_pattern from ..strings import get_string_id -from ..util import registry from .levenshtein import levenshtein_compare DEF PADDING = 5 @@ -87,9 +74,9 @@ cdef class Matcher: key (str): The match ID. RETURNS (bool): Whether the matcher contains rules for this match ID. """ - return self.has_key(key) + return self.has_key(key) # no-cython-lint: W601 - def add(self, key, patterns, *, on_match=None, greedy: str=None): + def add(self, key, patterns, *, on_match=None, greedy: str = None): """Add a match-rule to the matcher. A match-rule consists of: an ID key, an on_match callback, and one or more patterns. @@ -143,8 +130,13 @@ cdef class Matcher: key = self._normalize_key(key) for pattern in patterns: try: - specs = _preprocess_pattern(pattern, self.vocab, - self._extensions, self._extra_predicates, self._fuzzy_compare) + specs = _preprocess_pattern( + pattern, + self.vocab, + self._extensions, + self._extra_predicates, + self._fuzzy_compare + ) self.patterns.push_back(init_pattern(self.mem, key, specs)) for spec in specs: for attr, _ in spec[1]: @@ -168,7 +160,7 @@ cdef class Matcher: key (str): The ID of the match rule. """ norm_key = self._normalize_key(key) - if not norm_key in self._patterns: + if norm_key not in self._patterns: raise ValueError(Errors.E175.format(key=key)) self._patterns.pop(norm_key) self._callbacks.pop(norm_key) @@ -268,8 +260,15 @@ cdef class Matcher: if self.patterns.empty(): matches = [] else: - matches = find_matches(&self.patterns[0], self.patterns.size(), doclike, length, - extensions=self._extensions, predicates=self._extra_predicates, with_alignments=with_alignments) + matches = find_matches( + &self.patterns[0], + self.patterns.size(), + doclike, + length, + extensions=self._extensions, + predicates=self._extra_predicates, + with_alignments=with_alignments + ) final_matches = [] pairs_by_id = {} # For each key, either add all matches, or only the filtered, @@ -289,9 +288,9 @@ cdef class Matcher: memset(matched, 0, length * sizeof(matched[0])) span_filter = self._filter.get(key) if span_filter == "FIRST": - sorted_pairs = sorted(pairs, key=lambda x: (x[0], -x[1]), reverse=False) # sort by start + sorted_pairs = sorted(pairs, key=lambda x: (x[0], -x[1]), reverse=False) # sort by start elif span_filter == "LONGEST": - sorted_pairs = sorted(pairs, key=lambda x: (x[1]-x[0], -x[0]), reverse=True) # reverse sort by length + sorted_pairs = sorted(pairs, key=lambda x: (x[1]-x[0], -x[0]), reverse=True) # reverse sort by length else: raise ValueError(Errors.E947.format(expected=["FIRST", "LONGEST"], arg=span_filter)) for match in sorted_pairs: @@ -366,7 +365,6 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e cdef vector[MatchC] matches cdef vector[vector[MatchAlignmentC]] align_states cdef vector[vector[MatchAlignmentC]] align_matches - cdef PatternStateC state cdef int i, j, nr_extra_attr cdef Pool mem = Pool() output = [] @@ -388,14 +386,22 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e value = token.vocab.strings[value] extra_attr_values[i * nr_extra_attr + index] = value # Main loop - cdef int nr_predicate = len(predicates) for i in range(length): for j in range(n): states.push_back(PatternStateC(patterns[j], i, 0)) if with_alignments != 0: align_states.resize(states.size()) - transition_states(states, matches, align_states, align_matches, predicate_cache, - doclike[i], extra_attr_values, predicates, with_alignments) + transition_states( + states, + matches, + align_states, + align_matches, + predicate_cache, + doclike[i], + extra_attr_values, + predicates, + with_alignments + ) extra_attr_values += nr_extra_attr predicate_cache += len(predicates) # Handle matches that end in 0-width patterns @@ -421,18 +427,28 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e return output -cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& matches, - vector[vector[MatchAlignmentC]]& align_states, vector[vector[MatchAlignmentC]]& align_matches, - int8_t* cached_py_predicates, - Token token, const attr_t* extra_attrs, py_predicates, bint with_alignments) except *: +cdef void transition_states( + vector[PatternStateC]& states, + vector[MatchC]& matches, + vector[vector[MatchAlignmentC]]& align_states, + vector[vector[MatchAlignmentC]]& align_matches, + int8_t* cached_py_predicates, + Token token, + const attr_t* extra_attrs, + py_predicates, + bint with_alignments +) except *: cdef int q = 0 cdef vector[PatternStateC] new_states cdef vector[vector[MatchAlignmentC]] align_new_states - cdef int nr_predicate = len(py_predicates) for i in range(states.size()): if states[i].pattern.nr_py >= 1: - update_predicate_cache(cached_py_predicates, - states[i].pattern, token, py_predicates) + update_predicate_cache( + cached_py_predicates, + states[i].pattern, + token, + py_predicates + ) action = get_action(states[i], token.c, extra_attrs, cached_py_predicates) if action == REJECT: @@ -468,8 +484,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match align_new_states.push_back(align_states[q]) states[q].pattern += 1 if states[q].pattern.nr_py != 0: - update_predicate_cache(cached_py_predicates, - states[q].pattern, token, py_predicates) + update_predicate_cache( + cached_py_predicates, + states[q].pattern, + token, + py_predicates + ) action = get_action(states[q], token.c, extra_attrs, cached_py_predicates) # Update alignment before the transition of current state @@ -485,8 +505,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match ent_id = get_ent_id(state.pattern) if action == MATCH: matches.push_back( - MatchC(pattern_id=ent_id, start=state.start, - length=state.length+1)) + MatchC( + pattern_id=ent_id, + start=state.start, + length=state.length+1 + ) + ) # `align_matches` always corresponds to `matches` 1:1 if with_alignments != 0: align_matches.push_back(align_states[q]) @@ -494,23 +518,35 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match # push match without last token if length > 0 if state.length > 0: matches.push_back( - MatchC(pattern_id=ent_id, start=state.start, - length=state.length)) + MatchC( + pattern_id=ent_id, + start=state.start, + length=state.length + ) + ) # MATCH_DOUBLE emits matches twice, # add one more to align_matches in order to keep 1:1 relationship if with_alignments != 0: align_matches.push_back(align_states[q]) # push match with last token matches.push_back( - MatchC(pattern_id=ent_id, start=state.start, - length=state.length+1)) + MatchC( + pattern_id=ent_id, + start=state.start, + length=state.length + 1 + ) + ) # `align_matches` always corresponds to `matches` 1:1 if with_alignments != 0: align_matches.push_back(align_states[q]) elif action == MATCH_REJECT: matches.push_back( - MatchC(pattern_id=ent_id, start=state.start, - length=state.length)) + MatchC( + pattern_id=ent_id, + start=state.start, + length=state.length + ) + ) # `align_matches` always corresponds to `matches` 1:1 if with_alignments != 0: align_matches.push_back(align_states[q]) @@ -533,8 +569,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match align_states.push_back(align_new_states[i]) -cdef int update_predicate_cache(int8_t* cache, - const TokenPatternC* pattern, Token token, predicates) except -1: +cdef int update_predicate_cache( + int8_t* cache, + const TokenPatternC* pattern, + Token token, + predicates +) except -1: # If the state references any extra predicates, check whether they match. # These are cached, so that we don't call these potentially expensive # Python functions more than we need to. @@ -580,10 +620,12 @@ cdef void finish_states(vector[MatchC]& matches, vector[PatternStateC]& states, else: state.pattern += 1 - -cdef action_t get_action(PatternStateC state, - const TokenC* token, const attr_t* extra_attrs, - const int8_t* predicate_matches) nogil: +cdef action_t get_action( + PatternStateC state, + const TokenC * token, + const attr_t * extra_attrs, + const int8_t * predicate_matches +) nogil: """We need to consider: a) Does the token match the specification? [Yes, No] b) What's the quantifier? [1, 0+, ?] @@ -649,53 +691,56 @@ cdef action_t get_action(PatternStateC state, is_match = not is_match quantifier = ONE if quantifier == ONE: - if is_match and is_final: - # Yes, final: 1000 - return MATCH - elif is_match and not is_final: - # Yes, non-final: 0100 - return ADVANCE - elif not is_match and is_final: - # No, final: 0000 - return REJECT - else: - return REJECT + if is_match and is_final: + # Yes, final: 1000 + return MATCH + elif is_match and not is_final: + # Yes, non-final: 0100 + return ADVANCE + elif not is_match and is_final: + # No, final: 0000 + return REJECT + else: + return REJECT elif quantifier == ZERO_PLUS: - if is_match and is_final: - # Yes, final: 1001 - return MATCH_EXTEND - elif is_match and not is_final: - # Yes, non-final: 0011 - return RETRY_EXTEND - elif not is_match and is_final: - # No, final 2000 (note: Don't include last token!) - return MATCH_REJECT - else: - # No, non-final 0010 - return RETRY + if is_match and is_final: + # Yes, final: 1001 + return MATCH_EXTEND + elif is_match and not is_final: + # Yes, non-final: 0011 + return RETRY_EXTEND + elif not is_match and is_final: + # No, final 2000 (note: Don't include last token!) + return MATCH_REJECT + else: + # No, non-final 0010 + return RETRY elif quantifier == ZERO_ONE: - if is_match and is_final: - # Yes, final: 3000 - # To cater for a pattern ending in "?", we need to add - # a match both with and without the last token - return MATCH_DOUBLE - elif is_match and not is_final: - # Yes, non-final: 0110 - # We need both branches here, consider a pair like: - # pattern: .?b string: b - # If we 'ADVANCE' on the .?, we miss the match. - return RETRY_ADVANCE - elif not is_match and is_final: - # No, final 2000 (note: Don't include last token!) - return MATCH_REJECT - else: - # No, non-final 0010 - return RETRY + if is_match and is_final: + # Yes, final: 3000 + # To cater for a pattern ending in "?", we need to add + # a match both with and without the last token + return MATCH_DOUBLE + elif is_match and not is_final: + # Yes, non-final: 0110 + # We need both branches here, consider a pair like: + # pattern: .?b string: b + # If we 'ADVANCE' on the .?, we miss the match. + return RETRY_ADVANCE + elif not is_match and is_final: + # No, final 2000 (note: Don't include last token!) + return MATCH_REJECT + else: + # No, non-final 0010 + return RETRY -cdef int8_t get_is_match(PatternStateC state, - const TokenC* token, const attr_t* extra_attrs, - const int8_t* predicate_matches) nogil: +cdef int8_t get_is_match( + PatternStateC state, + const TokenC* token, + const attr_t* extra_attrs, + const int8_t* predicate_matches +) nogil: for i in range(state.pattern.nr_py): if predicate_matches[state.pattern.py_predicates[i]] == -1: return 0 @@ -860,7 +905,7 @@ class _FuzzyPredicate: self.is_extension = is_extension if self.predicate not in self.operators: raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate)) - fuzz = self.predicate[len("FUZZY"):] # number after prefix + fuzz = self.predicate[len("FUZZY"):] # number after prefix self.fuzzy = int(fuzz) if fuzz else -1 self.fuzzy_compare = fuzzy_compare self.key = _predicate_cache_key(self.attr, self.predicate, value, fuzzy=self.fuzzy) @@ -1082,7 +1127,7 @@ def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types, elif cls == _FuzzyPredicate: if isinstance(value, dict): # add predicates inside fuzzy operator - fuzz = type_[len("FUZZY"):] # number after prefix + fuzz = type_[len("FUZZY"):] # number after prefix fuzzy_val = int(fuzz) if fuzz else -1 output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types, extra_predicates, seen_predicates, @@ -1101,8 +1146,9 @@ def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types, return output -def _get_extension_extra_predicates(spec, extra_predicates, predicate_types, - seen_predicates): +def _get_extension_extra_predicates( + spec, extra_predicates, predicate_types, seen_predicates +): output = [] for attr, value in spec.items(): if isinstance(value, dict): @@ -1131,7 +1177,7 @@ def _get_operators(spec): return (ONE,) elif spec["OP"] in lookup: return lookup[spec["OP"]] - #Min_max {n,m} + # Min_max {n,m} elif spec["OP"].startswith("{") and spec["OP"].endswith("}"): # {n} --> {n,n} exactly n ONE,(n) # {n,m}--> {n,m} min of n, max of m ONE,(n),ZERO_ONE,(m) @@ -1142,8 +1188,8 @@ def _get_operators(spec): min_max = min_max if "," in min_max else f"{min_max},{min_max}" n, m = min_max.split(",") - #1. Either n or m is a blank string and the other is numeric -->isdigit - #2. Both are numeric and n <= m + # 1. Either n or m is a blank string and the other is numeric -->isdigit + # 2. Both are numeric and n <= m if (not n.isdecimal() and not m.isdecimal()) or (n.isdecimal() and m.isdecimal() and int(n) > int(m)): keys = ", ".join(lookup.keys()) + ", {n}, {n,m}, {n,}, {,m} where n and m are integers and n <= m " raise ValueError(Errors.E011.format(op=spec["OP"], opts=keys)) diff --git a/spacy/matcher/phrasematcher.pyx b/spacy/matcher/phrasematcher.pyx index c407cf1cc..26633e6d6 100644 --- a/spacy/matcher/phrasematcher.pyx +++ b/spacy/matcher/phrasematcher.pyx @@ -1,14 +1,12 @@ # cython: infer_types=True, profile=True -from libc.stdint cimport uintptr_t from preshed.maps cimport map_clear, map_get, map_init, map_iter, map_set import warnings -from ..attrs cimport DEP, LEMMA, MORPH, ORTH, POS, TAG +from ..attrs cimport DEP, LEMMA, MORPH, POS, TAG from ..attrs import IDS -from ..structs cimport TokenC from ..tokens.span cimport Span from ..tokens.token cimport Token from ..typedefs cimport attr_t diff --git a/spacy/ml/parser_model.pxd b/spacy/ml/parser_model.pxd index ca31c1699..4d2d7b3fe 100644 --- a/spacy/ml/parser_model.pxd +++ b/spacy/ml/parser_model.pxd @@ -40,11 +40,16 @@ cdef ActivationsC alloc_activations(SizesC n) nogil cdef void free_activations(const ActivationsC* A) nogil -cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states, - const WeightsC* W, SizesC n) nogil - +cdef void predict_states( + CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n +) nogil + cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil -cdef void cpu_log_loss(float* d_scores, - const float* costs, const int* is_valid, const float* scores, int O) nogil - +cdef void cpu_log_loss( + float* d_scores, + const float* costs, + const int* is_valid, + const float* scores, + int O +) nogil diff --git a/spacy/ml/parser_model.pyx b/spacy/ml/parser_model.pyx index 5cffc4c2d..ae60972aa 100644 --- a/spacy/ml/parser_model.pyx +++ b/spacy/ml/parser_model.pyx @@ -8,13 +8,13 @@ from thinc.backends.linalg cimport Vec, VecVec import numpy import numpy.random -from thinc.api import CupyOps, Model, NumpyOps, get_ops +from thinc.api import CupyOps, Model, NumpyOps from .. import util from ..errors import Errors from ..pipeline._parser_internals.stateclass cimport StateClass -from ..typedefs cimport class_t, hash_t, weight_t +from ..typedefs cimport weight_t cdef WeightsC get_c_weights(model) except *: @@ -78,33 +78,48 @@ cdef void resize_activations(ActivationsC* A, SizesC n) nogil: A.is_valid = calloc(n.states * n.classes, sizeof(A.is_valid[0])) A._max_size = n.states else: - A.token_ids = realloc(A.token_ids, - n.states * n.feats * sizeof(A.token_ids[0])) - A.scores = realloc(A.scores, - n.states * n.classes * sizeof(A.scores[0])) - A.unmaxed = realloc(A.unmaxed, - n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0])) - A.hiddens = realloc(A.hiddens, - n.states * n.hiddens * sizeof(A.hiddens[0])) - A.is_valid = realloc(A.is_valid, - n.states * n.classes * sizeof(A.is_valid[0])) + A.token_ids = realloc( + A.token_ids, n.states * n.feats * sizeof(A.token_ids[0]) + ) + A.scores = realloc( + A.scores, n.states * n.classes * sizeof(A.scores[0]) + ) + A.unmaxed = realloc( + A.unmaxed, n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]) + ) + A.hiddens = realloc( + A.hiddens, n.states * n.hiddens * sizeof(A.hiddens[0]) + ) + A.is_valid = realloc( + A.is_valid, n.states * n.classes * sizeof(A.is_valid[0]) + ) A._max_size = n.states A._curr_size = n.states -cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states, - const WeightsC* W, SizesC n) nogil: - cdef double one = 1.0 +cdef void predict_states( + CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n +) nogil: resize_activations(A, n) for i in range(n.states): states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats) memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float)) memset(A.hiddens, 0, n.states * n.hiddens * sizeof(float)) - sum_state_features(cblas, A.unmaxed, - W.feat_weights, A.token_ids, n.states, n.feats, n.hiddens * n.pieces) + sum_state_features( + cblas, + A.unmaxed, + W.feat_weights, + A.token_ids, + n.states, + n.feats, + n.hiddens * n.pieces + ) for i in range(n.states): - VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces], - W.feat_bias, 1., n.hiddens * n.pieces) + VecVec.add_i( + &A.unmaxed[i*n.hiddens*n.pieces], + W.feat_bias, 1., + n.hiddens * n.pieces + ) for j in range(n.hiddens): index = i * n.hiddens * n.pieces + j * n.pieces which = Vec.arg_max(&A.unmaxed[index], n.pieces) @@ -114,14 +129,15 @@ cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states, memcpy(A.scores, A.hiddens, n.states * n.classes * sizeof(float)) else: # Compute hidden-to-output - sgemm(cblas)(False, True, n.states, n.classes, n.hiddens, + sgemm(cblas)( + False, True, n.states, n.classes, n.hiddens, 1.0, A.hiddens, n.hiddens, W.hidden_weights, n.hiddens, - 0.0, A.scores, n.classes) + 0.0, A.scores, n.classes + ) # Add bias for i in range(n.states): - VecVec.add_i(&A.scores[i*n.classes], - W.hidden_bias, 1., n.classes) + VecVec.add_i(&A.scores[i*n.classes], W.hidden_bias, 1., n.classes) # Set unseen classes to minimum value i = 0 min_ = A.scores[0] @@ -134,9 +150,16 @@ cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states, A.scores[i*n.classes+j] = min_ -cdef void sum_state_features(CBlas cblas, float* output, - const float* cached, const int* token_ids, int B, int F, int O) nogil: - cdef int idx, b, f, i +cdef void sum_state_features( + CBlas cblas, + float* output, + const float* cached, + const int* token_ids, + int B, + int F, + int O +) nogil: + cdef int idx, b, f cdef const float* feature padding = cached cached += F * O @@ -153,9 +176,13 @@ cdef void sum_state_features(CBlas cblas, float* output, token_ids += F -cdef void cpu_log_loss(float* d_scores, - const float* costs, const int* is_valid, const float* scores, - int O) nogil: +cdef void cpu_log_loss( + float* d_scores, + const float* costs, + const int* is_valid, + const float* scores, + int O +) nogil: """Do multi-label log loss""" cdef double max_, gmax, Z, gZ best = arg_max_if_gold(scores, costs, is_valid, O) @@ -179,8 +206,9 @@ cdef void cpu_log_loss(float* d_scores, d_scores[i] = exp(scores[i]-max_) / Z -cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, - const int* is_valid, int n) nogil: +cdef int arg_max_if_gold( + const weight_t* scores, const weight_t* costs, const int* is_valid, int n +) nogil: # Find minimum cost cdef float cost = 1 for i in range(n): @@ -204,10 +232,17 @@ cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) no return best - class ParserStepModel(Model): - def __init__(self, docs, layers, *, has_upper, unseen_classes=None, train=True, - dropout=0.1): + def __init__( + self, + docs, + layers, + *, + has_upper, + unseen_classes=None, + train=True, + dropout=0.1 + ): Model.__init__(self, name="parser_step_model", forward=step_forward) self.attrs["has_upper"] = has_upper self.attrs["dropout_rate"] = dropout @@ -268,8 +303,10 @@ class ParserStepModel(Model): return ids def backprop_step(self, token_ids, d_vector, get_d_tokvecs): - if isinstance(self.state2vec.ops, CupyOps) \ - and not isinstance(token_ids, self.state2vec.ops.xp.ndarray): + if ( + isinstance(self.state2vec.ops, CupyOps) + and not isinstance(token_ids, self.state2vec.ops.xp.ndarray) + ): # Move token_ids and d_vector to GPU, asynchronously self.backprops.append(( util.get_async(self.cuda_stream, token_ids), @@ -279,7 +316,6 @@ class ParserStepModel(Model): else: self.backprops.append((token_ids, d_vector, get_d_tokvecs)) - def finish_steps(self, golds): # Add a padding vector to the d_tokvecs gradient, so that missing # values don't affect the real gradient. @@ -292,14 +328,15 @@ class ParserStepModel(Model): ids = ids.flatten() d_state_features = d_state_features.reshape( (ids.size, d_state_features.shape[2])) - self.ops.scatter_add(d_tokvecs, ids, - d_state_features) + self.ops.scatter_add(d_tokvecs, ids, d_state_features) # Padded -- see update() self.bp_tokvecs(d_tokvecs[:-1]) return d_tokvecs + NUMPY_OPS = NumpyOps() + def step_forward(model: ParserStepModel, states, is_train): token_ids = model.get_token_ids(states) vector, get_d_tokvecs = model.state2vec(token_ids, is_train) @@ -312,7 +349,7 @@ def step_forward(model: ParserStepModel, states, is_train): scores, get_d_vector = model.vec2scores(vector, is_train) else: scores = NumpyOps().asarray(vector) - get_d_vector = lambda d_scores: d_scores + get_d_vector = lambda d_scores: d_scores # no-cython-lint: E731 # If the class is unseen, make sure its score is minimum scores[:, model._class_mask == 0] = numpy.nanmin(scores) @@ -448,9 +485,11 @@ cdef class precompute_hiddens: feat_weights = self.get_feat_weights() cdef int[:, ::1] ids = token_ids - sum_state_features(cblas, state_vector.data, - feat_weights, &ids[0,0], - token_ids.shape[0], self.nF, self.nO*self.nP) + sum_state_features( + cblas, state_vector.data, + feat_weights, &ids[0, 0], + token_ids.shape[0], self.nF, self.nO*self.nP + ) state_vector += self.bias state_vector, bp_nonlinearity = self._nonlinearity(state_vector) @@ -475,7 +514,7 @@ cdef class precompute_hiddens: def backprop_maxout(d_best): return self.ops.backprop_maxout(d_best, mask, self.nP) - + return state_vector, backprop_maxout def _relu_nonlinearity(self, state_vector): @@ -489,5 +528,5 @@ cdef class precompute_hiddens: def backprop_relu(d_best): d_best *= mask return d_best.reshape((d_best.shape + (1,))) - + return state_vector, backprop_relu diff --git a/spacy/morphology.pxd b/spacy/morphology.pxd index 968764b82..ee43aa4ec 100644 --- a/spacy/morphology.pxd +++ b/spacy/morphology.pxd @@ -11,7 +11,7 @@ from .typedefs cimport attr_t, hash_t cdef class Morphology: cdef readonly Pool mem cdef readonly StringStore strings - cdef PreshMap tags # Keyed by hash, value is pointer to tag + cdef PreshMap tags # Keyed by hash, value is pointer to tag cdef MorphAnalysisC create_morph_tag(self, field_feature_pairs) except * cdef int insert(self, MorphAnalysisC tag) except -1 @@ -20,4 +20,8 @@ cdef class Morphology: cdef int check_feature(const MorphAnalysisC* morph, attr_t feature) nogil cdef list list_features(const MorphAnalysisC* morph) cdef np.ndarray get_by_field(const MorphAnalysisC* morph, attr_t field) -cdef int get_n_by_field(attr_t* results, const MorphAnalysisC* morph, attr_t field) nogil +cdef int get_n_by_field( + attr_t* results, + const MorphAnalysisC* morph, + attr_t field, +) nogil diff --git a/spacy/morphology.pyx b/spacy/morphology.pyx index 1062fff09..ecbbed729 100644 --- a/spacy/morphology.pyx +++ b/spacy/morphology.pyx @@ -83,10 +83,11 @@ cdef class Morphology: features = self.normalize_attrs(features) string_features = {self.strings.as_string(field): self.strings.as_string(values) for field, values in features.items()} # normalized UFEATS string with sorted fields and values - norm_feats_string = self.FEATURE_SEP.join(sorted([ - self.FIELD_SEP.join([field, values]) - for field, values in string_features.items() - ])) + norm_feats_string = self.FEATURE_SEP.join( + sorted( + [self.FIELD_SEP.join([field, values]) for field, values in string_features.items()] + ) + ) return norm_feats_string or self.EMPTY_MORPH def normalize_attrs(self, attrs): @@ -192,6 +193,7 @@ cdef int get_n_by_field(attr_t* results, const MorphAnalysisC* morph, attr_t fie n_results += 1 return n_results + def unpickle_morphology(strings, tags): cdef Morphology morphology = Morphology(strings) for tag in tags: diff --git a/spacy/parts_of_speech.pxd b/spacy/parts_of_speech.pxd index a0b2567f1..b5423d113 100644 --- a/spacy/parts_of_speech.pxd +++ b/spacy/parts_of_speech.pxd @@ -8,7 +8,7 @@ cpdef enum univ_pos_t: ADV AUX CONJ - CCONJ # U20 + CCONJ # U20 DET INTJ NOUN diff --git a/spacy/pipeline/_edit_tree_internals/edit_trees.pxd b/spacy/pipeline/_edit_tree_internals/edit_trees.pxd index 3d63af921..41acd2b07 100644 --- a/spacy/pipeline/_edit_tree_internals/edit_trees.pxd +++ b/spacy/pipeline/_edit_tree_internals/edit_trees.pxd @@ -46,11 +46,18 @@ cdef struct EditTreeC: bint is_match_node NodeC inner -cdef inline EditTreeC edittree_new_match(len_t prefix_len, len_t suffix_len, - uint32_t prefix_tree, uint32_t suffix_tree): - cdef MatchNodeC match_node = MatchNodeC(prefix_len=prefix_len, - suffix_len=suffix_len, prefix_tree=prefix_tree, - suffix_tree=suffix_tree) +cdef inline EditTreeC edittree_new_match( + len_t prefix_len, + len_t suffix_len, + uint32_t prefix_tree, + uint32_t suffix_tree +): + cdef MatchNodeC match_node = MatchNodeC( + prefix_len=prefix_len, + suffix_len=suffix_len, + prefix_tree=prefix_tree, + suffix_tree=suffix_tree + ) cdef NodeC inner = NodeC(match_node=match_node) return EditTreeC(is_match_node=True, inner=inner) diff --git a/spacy/pipeline/_edit_tree_internals/edit_trees.pyx b/spacy/pipeline/_edit_tree_internals/edit_trees.pyx index daab0d204..78cd25622 100644 --- a/spacy/pipeline/_edit_tree_internals/edit_trees.pyx +++ b/spacy/pipeline/_edit_tree_internals/edit_trees.pyx @@ -5,8 +5,6 @@ from libc.string cimport memset from libcpp.pair cimport pair from libcpp.vector cimport vector -from pathlib import Path - from ...typedefs cimport hash_t from ... import util @@ -25,17 +23,16 @@ cdef LCS find_lcs(str source, str target): target (str): The second string. RETURNS (LCS): The spans of the longest common subsequences. """ - cdef Py_ssize_t source_len = len(source) cdef Py_ssize_t target_len = len(target) - cdef size_t longest_align = 0; + cdef size_t longest_align = 0 cdef int source_idx, target_idx cdef LCS lcs cdef Py_UCS4 source_cp, target_cp memset(&lcs, 0, sizeof(lcs)) - cdef vector[size_t] prev_aligns = vector[size_t](target_len); - cdef vector[size_t] cur_aligns = vector[size_t](target_len); + cdef vector[size_t] prev_aligns = vector[size_t](target_len) + cdef vector[size_t] cur_aligns = vector[size_t](target_len) for (source_idx, source_cp) in enumerate(source): for (target_idx, target_cp) in enumerate(target): @@ -89,7 +86,7 @@ cdef class EditTrees: cdef LCS lcs = find_lcs(form, lemma) cdef EditTreeC tree - cdef uint32_t tree_id, prefix_tree, suffix_tree + cdef uint32_t prefix_tree, suffix_tree if lcs_is_empty(lcs): tree = edittree_new_subst(self.strings.add(form), self.strings.add(lemma)) else: @@ -108,7 +105,7 @@ cdef class EditTrees: return self._tree_id(tree) cdef uint32_t _tree_id(self, EditTreeC tree): - # If this tree has been constructed before, return its identifier. + # If this tree has been constructed before, return its identifier. cdef hash_t hash = edittree_hash(tree) cdef unordered_map[hash_t, uint32_t].iterator iter = self.map.find(hash) if iter != self.map.end(): @@ -289,6 +286,7 @@ def _tree2dict(tree): tree = tree["inner"]["subst_node"] return(dict(tree)) + def _dict2tree(tree): errors = validate_edit_tree(tree) if errors: diff --git a/spacy/pipeline/_parser_internals/_beam_utils.pyx b/spacy/pipeline/_parser_internals/_beam_utils.pyx index 04dd3f11e..de8f0bf7b 100644 --- a/spacy/pipeline/_parser_internals/_beam_utils.pyx +++ b/spacy/pipeline/_parser_internals/_beam_utils.pyx @@ -1,17 +1,14 @@ # cython: infer_types=True # cython: profile=True -cimport numpy as np - import numpy -from cpython.ref cimport Py_XDECREF, PyObject from thinc.extra.search cimport Beam from thinc.extra.search import MaxViolation from thinc.extra.search cimport MaxViolation -from ...typedefs cimport class_t, hash_t +from ...typedefs cimport class_t from .transition_system cimport Transition, TransitionSystem from ...errors import Errors @@ -146,7 +143,6 @@ def update_beam(TransitionSystem moves, states, golds, model, int width, beam_de cdef MaxViolation violn pbeam = BeamBatch(moves, states, golds, width=width, density=beam_density) gbeam = BeamBatch(moves, states, golds, width=width, density=0.0) - cdef StateClass state beam_maps = [] backprops = [] violns = [MaxViolation() for _ in range(len(states))] diff --git a/spacy/pipeline/_parser_internals/_state.pxd b/spacy/pipeline/_parser_internals/_state.pxd index 24acc350c..c063cf97c 100644 --- a/spacy/pipeline/_parser_internals/_state.pxd +++ b/spacy/pipeline/_parser_internals/_state.pxd @@ -277,7 +277,6 @@ cdef cppclass StateC: return n - int n_L(int head) nogil const: return n_arcs(this._left_arcs, head) diff --git a/spacy/pipeline/_parser_internals/arc_eager.pyx b/spacy/pipeline/_parser_internals/arc_eager.pyx index 2c9eb0ff5..bcb4626fb 100644 --- a/spacy/pipeline/_parser_internals/arc_eager.pyx +++ b/spacy/pipeline/_parser_internals/arc_eager.pyx @@ -9,7 +9,7 @@ from ...strings cimport hash_string from ...structs cimport TokenC from ...tokens.doc cimport Doc, set_children_from_heads from ...tokens.token cimport MISSING_DEP -from ...typedefs cimport attr_t, hash_t +from ...typedefs cimport attr_t from ...training import split_bilu_label @@ -68,8 +68,9 @@ cdef struct GoldParseStateC: weight_t pop_cost -cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state, - heads, labels, sent_starts) except *: +cdef GoldParseStateC create_gold_state( + Pool mem, const StateC* state, heads, labels, sent_starts +) except *: cdef GoldParseStateC gs gs.length = len(heads) gs.stride = 1 @@ -82,7 +83,7 @@ cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state, gs.n_kids_in_stack = mem.alloc(gs.length, sizeof(gs.n_kids_in_stack[0])) for i, is_sent_start in enumerate(sent_starts): - if is_sent_start == True: + if is_sent_start is True: gs.state_bits[i] = set_state_flag( gs.state_bits[i], IS_SENT_START, @@ -210,6 +211,7 @@ cdef class ArcEagerGold: def update(self, StateClass stcls): update_gold_state(&self.c, stcls.c) + def _get_aligned_sent_starts(example): """Get list of SENT_START attributes aligned to the predicted tokenization. If the reference has not sentence starts, return a list of None values. @@ -524,7 +526,6 @@ cdef class Break: """ @staticmethod cdef bint is_valid(const StateC* st, attr_t label) nogil: - cdef int i if st.buffer_length() < 2: return False elif st.B(1) != st.B(0) + 1: @@ -556,8 +557,8 @@ cdef class Break: cost -= 1 if gold.heads[si] == b0: cost -= 1 - if not is_sent_start(gold, state.B(1)) \ - and not is_sent_start_unknown(gold, state.B(1)): + if not is_sent_start(gold, state.B(1)) and\ + not is_sent_start_unknown(gold, state.B(1)): cost += 1 return cost @@ -803,7 +804,6 @@ cdef class ArcEager(TransitionSystem): raise TypeError(Errors.E909.format(name="ArcEagerGold")) cdef ArcEagerGold gold_ = gold gold_state = gold_.c - n_gold = 0 if self.c[i].is_valid(stcls.c, self.c[i].label): cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label) else: @@ -875,7 +875,7 @@ cdef class ArcEager(TransitionSystem): print("Gold") for token in example.y: print(token.i, token.text, token.dep_, token.head.text) - aligned_heads, aligned_labels = example.get_aligned_parse() + aligned_heads, _aligned_labels = example.get_aligned_parse() print("Aligned heads") for i, head in enumerate(aligned_heads): print(example.x[i], example.x[head] if head is not None else "__") diff --git a/spacy/pipeline/_parser_internals/ner.pyx b/spacy/pipeline/_parser_internals/ner.pyx index e1edb4464..6c4f8e245 100644 --- a/spacy/pipeline/_parser_internals/ner.pyx +++ b/spacy/pipeline/_parser_internals/ner.pyx @@ -1,6 +1,3 @@ -import os -import random - from cymem.cymem cimport Pool from libc.stdint cimport int32_t @@ -14,7 +11,7 @@ from ...tokens.span import Span from ...attrs cimport IS_SPACE from ...lexeme cimport Lexeme -from ...structs cimport SpanC, TokenC +from ...structs cimport SpanC from ...tokens.span cimport Span from ...typedefs cimport attr_t, weight_t @@ -141,11 +138,10 @@ cdef class BiluoPushDown(TransitionSystem): OUT: Counter() } actions[OUT][''] = 1 # Represents a token predicted to be outside of any entity - actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity + actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity for entity_type in kwargs.get('entity_types', []): for action in (BEGIN, IN, LAST, UNIT): actions[action][entity_type] = 1 - moves = ('M', 'B', 'I', 'L', 'U') for example in kwargs.get('examples', []): for token in example.y: ent_type = token.ent_type_ @@ -164,7 +160,7 @@ cdef class BiluoPushDown(TransitionSystem): if token.ent_type: labels.add(token.ent_type_) return labels - + def move_name(self, int move, attr_t label): if move == OUT: return 'O' @@ -325,7 +321,6 @@ cdef class BiluoPushDown(TransitionSystem): raise TypeError(Errors.E909.format(name="BiluoGold")) cdef BiluoGold gold_ = gold gold_state = gold_.c - n_gold = 0 if self.c[i].is_valid(stcls.c, self.c[i].label): cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label) else: @@ -486,10 +481,8 @@ cdef class In: @staticmethod cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil: gold = _gold - move = IN cdef int next_act = gold.ner[s.B(1)].move if s.B(1) >= 0 else OUT cdef int g_act = gold.ner[s.B(0)].move - cdef attr_t g_tag = gold.ner[s.B(0)].label cdef bint is_sunk = _entity_is_sunk(s, gold.ner) if g_act == MISSING: @@ -549,12 +542,10 @@ cdef class Last: @staticmethod cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil: gold = _gold - move = LAST b0 = s.B(0) ent_start = s.E(0) cdef int g_act = gold.ner[b0].move - cdef attr_t g_tag = gold.ner[b0].label cdef int cost = 0 @@ -650,7 +641,6 @@ cdef class Unit: cost += 1 break return cost - cdef class Out: @@ -675,7 +665,6 @@ cdef class Out: cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil: gold = _gold cdef int g_act = gold.ner[s.B(0)].move - cdef attr_t g_tag = gold.ner[s.B(0)].label cdef weight_t cost = 0 if g_act == MISSING: pass diff --git a/spacy/pipeline/_parser_internals/nonproj.pyx b/spacy/pipeline/_parser_internals/nonproj.pyx index 66f423b3b..93ad14feb 100644 --- a/spacy/pipeline/_parser_internals/nonproj.pyx +++ b/spacy/pipeline/_parser_internals/nonproj.pyx @@ -125,14 +125,17 @@ def decompose(label): def is_decorated(label): return DELIMITER in label + def count_decorated_labels(gold_data): freqs = {} for example in gold_data: proj_heads, deco_deps = projectivize(example.get_aligned("HEAD"), example.get_aligned("DEP")) # set the label to ROOT for each root dependent - deco_deps = ['ROOT' if head == i else deco_deps[i] - for i, head in enumerate(proj_heads)] + deco_deps = [ + 'ROOT' if head == i else deco_deps[i] + for i, head in enumerate(proj_heads) + ] # count label frequencies for label in deco_deps: if is_decorated(label): @@ -160,9 +163,9 @@ def projectivize(heads, labels): cdef vector[int] _heads_to_c(heads): - cdef vector[int] c_heads; + cdef vector[int] c_heads for head in heads: - if head == None: + if head is None: c_heads.push_back(-1) else: assert head < len(heads) @@ -199,6 +202,7 @@ def _decorate(heads, proj_heads, labels): deco_labels.append(labels[tokenid]) return deco_labels + def get_smallest_nonproj_arc_slow(heads): cdef vector[int] c_heads = _heads_to_c(heads) return _get_smallest_nonproj_arc(c_heads) diff --git a/spacy/pipeline/_parser_internals/stateclass.pyx b/spacy/pipeline/_parser_internals/stateclass.pyx index 0a2657af1..fdb5004bb 100644 --- a/spacy/pipeline/_parser_internals/stateclass.pyx +++ b/spacy/pipeline/_parser_internals/stateclass.pyx @@ -1,6 +1,4 @@ # cython: infer_types=True -import numpy - from libcpp.vector cimport vector from ...tokens.doc cimport Doc @@ -38,11 +36,11 @@ cdef class StateClass: cdef vector[ArcC] arcs self.c.get_arcs(&arcs) return list(arcs) - #py_arcs = [] - #for arc in arcs: - # if arc.head != -1 and arc.child != -1: - # py_arcs.append((arc.head, arc.child, arc.label)) - #return arcs + # py_arcs = [] + # for arc in arcs: + # if arc.head != -1 and arc.child != -1: + # py_arcs.append((arc.head, arc.child, arc.label)) + # return arcs def add_arc(self, int head, int child, int label): self.c.add_arc(head, child, label) @@ -52,10 +50,10 @@ cdef class StateClass: def H(self, int child): return self.c.H(child) - + def L(self, int head, int idx): return self.c.L(head, idx) - + def R(self, int head, int idx): return self.c.R(head, idx) @@ -98,7 +96,7 @@ cdef class StateClass: def H(self, int i): return self.c.H(i) - + def E(self, int i): return self.c.E(i) @@ -116,7 +114,7 @@ cdef class StateClass: def H_(self, int i): return self.doc[self.c.H(i)] - + def E_(self, int i): return self.doc[self.c.E(i)] @@ -125,7 +123,7 @@ cdef class StateClass: def R_(self, int i, int idx): return self.doc[self.c.R(i, idx)] - + def empty(self): return self.c.empty() @@ -134,7 +132,7 @@ cdef class StateClass: def at_break(self): return False - #return self.c.at_break() + # return self.c.at_break() def has_head(self, int i): return self.c.has_head(i) diff --git a/spacy/pipeline/_parser_internals/transition_system.pxd b/spacy/pipeline/_parser_internals/transition_system.pxd index ce17480d4..04cd10d88 100644 --- a/spacy/pipeline/_parser_internals/transition_system.pxd +++ b/spacy/pipeline/_parser_internals/transition_system.pxd @@ -20,11 +20,15 @@ cdef struct Transition: int (*do)(StateC* state, attr_t label) nogil -ctypedef weight_t (*get_cost_func_t)(const StateC* state, const void* gold, - attr_tlabel) nogil -ctypedef weight_t (*move_cost_func_t)(const StateC* state, const void* gold) nogil -ctypedef weight_t (*label_cost_func_t)(const StateC* state, const void* - gold, attr_t label) nogil +ctypedef weight_t (*get_cost_func_t)( + const StateC* state, const void* gold, attr_tlabel +) nogil +ctypedef weight_t (*move_cost_func_t)( + const StateC* state, const void* gold +) nogil +ctypedef weight_t (*label_cost_func_t)( + const StateC* state, const void* gold, attr_t label +) nogil ctypedef int (*do_func_t)(StateC* state, attr_t label) nogil diff --git a/spacy/pipeline/_parser_internals/transition_system.pyx b/spacy/pipeline/_parser_internals/transition_system.pyx index 053c87f22..aabbdfa24 100644 --- a/spacy/pipeline/_parser_internals/transition_system.pyx +++ b/spacy/pipeline/_parser_internals/transition_system.pyx @@ -8,9 +8,7 @@ from collections import Counter import srsly from ...structs cimport TokenC -from ...tokens.doc cimport Doc from ...typedefs cimport attr_t, weight_t -from . cimport _beam_utils from .stateclass cimport StateClass from ... import util @@ -231,7 +229,6 @@ cdef class TransitionSystem: return self def to_bytes(self, exclude=tuple()): - transitions = [] serializers = { 'moves': lambda: srsly.json_dumps(self.labels), 'strings': lambda: self.strings.to_bytes(), diff --git a/spacy/pipeline/dep_parser.pyx b/spacy/pipeline/dep_parser.pyx index cb896c385..57f091788 100644 --- a/spacy/pipeline/dep_parser.pyx +++ b/spacy/pipeline/dep_parser.pyx @@ -1,6 +1,6 @@ # cython: infer_types=True, profile=True, binding=True from collections import defaultdict -from typing import Callable, Iterable, Optional +from typing import Callable, Optional from thinc.api import Config, Model @@ -124,6 +124,7 @@ def make_parser( scorer=scorer, ) + @Language.factory( "beam_parser", assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"], diff --git a/spacy/pipeline/morphologizer.pyx b/spacy/pipeline/morphologizer.pyx index 4ca0ce165..7ca3908bd 100644 --- a/spacy/pipeline/morphologizer.pyx +++ b/spacy/pipeline/morphologizer.pyx @@ -2,7 +2,6 @@ from itertools import islice from typing import Callable, Dict, Optional, Union -import srsly from thinc.api import Config, Model, SequenceCategoricalCrossentropy from ..morphology cimport Morphology @@ -14,10 +13,8 @@ from ..errors import Errors from ..language import Language from ..parts_of_speech import IDS as POS_IDS from ..scorer import Scorer -from ..symbols import POS from ..training import validate_examples, validate_get_examples from ..util import registry -from .pipe import deserialize_config from .tagger import Tagger # See #9050 @@ -76,8 +73,11 @@ def morphologizer_score(examples, **kwargs): results = {} results.update(Scorer.score_token_attr(examples, "pos", **kwargs)) results.update(Scorer.score_token_attr(examples, "morph", getter=morph_key_getter, **kwargs)) - results.update(Scorer.score_token_attr_per_feat(examples, - "morph", getter=morph_key_getter, **kwargs)) + results.update( + Scorer.score_token_attr_per_feat( + examples, "morph", getter=morph_key_getter, **kwargs + ) + ) return results @@ -233,7 +233,6 @@ class Morphologizer(Tagger): if isinstance(docs, Doc): docs = [docs] cdef Doc doc - cdef Vocab vocab = self.vocab cdef bint overwrite = self.cfg["overwrite"] cdef bint extend = self.cfg["extend"] labels = self.labels diff --git a/spacy/pipeline/multitask.pyx b/spacy/pipeline/multitask.pyx index 6b62c0811..2a62a50d5 100644 --- a/spacy/pipeline/multitask.pyx +++ b/spacy/pipeline/multitask.pyx @@ -4,13 +4,10 @@ from typing import Optional import numpy from thinc.api import Config, CosineDistance, Model, set_dropout_rate, to_categorical -from ..tokens.doc cimport Doc - -from ..attrs import ID, POS +from ..attrs import ID from ..errors import Errors from ..language import Language from ..training import validate_examples -from ._parser_internals import nonproj from .tagger import Tagger from .trainable_pipe import TrainablePipe @@ -103,10 +100,9 @@ class MultitaskObjective(Tagger): cdef int idx = 0 correct = numpy.zeros((scores.shape[0],), dtype="i") guesses = scores.argmax(axis=1) - docs = [eg.predicted for eg in examples] for i, eg in enumerate(examples): # Handles alignment for tokenization differences - doc_annots = eg.get_aligned() # TODO + _doc_annots = eg.get_aligned() # TODO for j in range(len(eg.predicted)): tok_annots = {key: values[j] for key, values in tok_annots.items()} label = self.make_label(j, tok_annots) @@ -206,7 +202,6 @@ class ClozeMultitask(TrainablePipe): losses[self.name] = 0. set_dropout_rate(self.model, drop) validate_examples(examples, "ClozeMultitask.rehearse") - docs = [eg.predicted for eg in examples] predictions, bp_predictions = self.model.begin_update() loss, d_predictions = self.get_loss(examples, self.vocab.vectors.data, predictions) bp_predictions(d_predictions) diff --git a/spacy/pipeline/ner.pyx b/spacy/pipeline/ner.pyx index 8dd6c3c43..15c092ae9 100644 --- a/spacy/pipeline/ner.pyx +++ b/spacy/pipeline/ner.pyx @@ -1,6 +1,6 @@ # cython: infer_types=True, profile=True, binding=True from collections import defaultdict -from typing import Callable, Iterable, Optional +from typing import Callable, Optional from thinc.api import Config, Model @@ -10,7 +10,7 @@ from ._parser_internals.ner cimport BiluoPushDown from .transition_parser cimport Parser from ..language import Language -from ..scorer import PRFScore, get_ner_prf +from ..scorer import get_ner_prf from ..training import remove_bilu_prefix from ..util import registry @@ -100,6 +100,7 @@ def make_ner( scorer=scorer, ) + @Language.factory( "beam_ner", assigns=["doc.ents", "token.ent_iob", "token.ent_type"], diff --git a/spacy/pipeline/pipe.pyx b/spacy/pipeline/pipe.pyx index 42f518882..90775c465 100644 --- a/spacy/pipeline/pipe.pyx +++ b/spacy/pipeline/pipe.pyx @@ -1,6 +1,6 @@ # cython: infer_types=True, profile=True, binding=True import warnings -from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple, Union +from typing import Callable, Dict, Iterable, Iterator, Tuple, Union import srsly @@ -40,7 +40,7 @@ cdef class Pipe: """ raise NotImplementedError(Errors.E931.format(parent="Pipe", method="__call__", name=self.name)) - def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]: + def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]: """Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all components are applied to the Doc. @@ -59,7 +59,7 @@ cdef class Pipe: except Exception as e: error_handler(self.name, self, [doc], e) - def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None): + def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None): """Initialize the pipe. For non-trainable components, this method is optional. For trainable components, which should inherit from the subclass TrainablePipe, the provided data examples diff --git a/spacy/pipeline/sentencizer.pyx b/spacy/pipeline/sentencizer.pyx index 2fe7e1540..76f296644 100644 --- a/spacy/pipeline/sentencizer.pyx +++ b/spacy/pipeline/sentencizer.pyx @@ -7,13 +7,13 @@ from ..tokens.doc cimport Doc from .. import util from ..language import Language -from ..scorer import Scorer from .pipe import Pipe from .senter import senter_score # see #9050 BACKWARD_OVERWRITE = False + @Language.factory( "sentencizer", assigns=["token.is_sent_start", "doc.sents"], @@ -36,17 +36,19 @@ class Sentencizer(Pipe): DOCS: https://spacy.io/api/sentencizer """ - default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹', - '।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄', - '᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿', - '‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶', - '꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒', - '﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀', - '𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼', - '𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐', - '𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂', - '𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈', - '｡', '。'] + default_punct_chars = [ + '!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹', + '।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄', + '᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿', + '‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶', + '꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒', + '﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀', + '𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼', + '𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐', + '𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂', + '𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈', + '｡', '。' + ] def __init__( self, @@ -128,7 +130,6 @@ class Sentencizer(Pipe): if isinstance(docs, Doc): docs = [docs] cdef Doc doc - cdef int idx = 0 for i, doc in enumerate(docs): doc_tag_ids = batch_tag_ids[i] for j, tag_id in enumerate(doc_tag_ids): @@ -169,7 +170,6 @@ class Sentencizer(Pipe): path = path.with_suffix(".json") srsly.write_json(path, {"punct_chars": list(self.punct_chars), "overwrite": self.overwrite}) - def from_disk(self, path, *, exclude=tuple()): """Load the sentencizer from disk. diff --git a/spacy/pipeline/senter.pyx b/spacy/pipeline/senter.pyx index 26f98ba59..37ddcc3c0 100644 --- a/spacy/pipeline/senter.pyx +++ b/spacy/pipeline/senter.pyx @@ -2,7 +2,6 @@ from itertools import islice from typing import Callable, Optional -import srsly from thinc.api import Config, Model, SequenceCategoricalCrossentropy from ..tokens.doc cimport Doc diff --git a/spacy/pipeline/tagger.pyx b/spacy/pipeline/tagger.pyx index 47aae2bb7..4c5265a78 100644 --- a/spacy/pipeline/tagger.pyx +++ b/spacy/pipeline/tagger.pyx @@ -1,26 +1,18 @@ # cython: infer_types=True, profile=True, binding=True -import warnings from itertools import islice from typing import Callable, Optional import numpy -import srsly from thinc.api import Config, Model, SequenceCategoricalCrossentropy, set_dropout_rate -from thinc.types import Floats2d -from ..morphology cimport Morphology from ..tokens.doc cimport Doc -from ..vocab cimport Vocab from .. import util -from ..attrs import ID, POS -from ..errors import Errors, Warnings +from ..errors import Errors from ..language import Language -from ..parts_of_speech import X from ..scorer import Scorer from ..training import validate_examples, validate_get_examples from ..util import registry -from .pipe import deserialize_config from .trainable_pipe import TrainablePipe # See #9050 @@ -169,7 +161,6 @@ class Tagger(TrainablePipe): if isinstance(docs, Doc): docs = [docs] cdef Doc doc - cdef Vocab vocab = self.vocab cdef bint overwrite = self.cfg["overwrite"] labels = self.labels for i, doc in enumerate(docs): diff --git a/spacy/pipeline/trainable_pipe.pyx b/spacy/pipeline/trainable_pipe.pyx index 7aa91ac16..e5865e070 100644 --- a/spacy/pipeline/trainable_pipe.pyx +++ b/spacy/pipeline/trainable_pipe.pyx @@ -55,7 +55,7 @@ cdef class TrainablePipe(Pipe): except Exception as e: error_handler(self.name, self, [doc], e) - def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]: + def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]: """Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all components are applied to the Doc. @@ -102,9 +102,9 @@ cdef class TrainablePipe(Pipe): def update(self, examples: Iterable["Example"], *, - drop: float=0.0, - sgd: Optimizer=None, - losses: Optional[Dict[str, float]]=None) -> Dict[str, float]: + drop: float = 0.0, + sgd: Optimizer = None, + losses: Optional[Dict[str, float]] = None) -> Dict[str, float]: """Learn from a batch of documents and gold-standard information, updating the pipe's model. Delegates to predict and get_loss. @@ -138,8 +138,8 @@ cdef class TrainablePipe(Pipe): def rehearse(self, examples: Iterable[Example], *, - sgd: Optimizer=None, - losses: Dict[str, float]=None, + sgd: Optimizer = None, + losses: Dict[str, float] = None, **config) -> Dict[str, float]: """Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the current model to make predictions similar to an initial model, @@ -177,7 +177,7 @@ cdef class TrainablePipe(Pipe): """ return util.create_default_optimizer() - def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None): + def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None): """Initialize the pipe for training, using data examples if available. This method needs to be implemented by each TrainablePipe component, ensuring the internal model (if available) is initialized properly diff --git a/spacy/pipeline/transition_parser.pxd b/spacy/pipeline/transition_parser.pxd index e5e88d521..7ddb91e01 100644 --- a/spacy/pipeline/transition_parser.pxd +++ b/spacy/pipeline/transition_parser.pxd @@ -13,8 +13,18 @@ cdef class Parser(TrainablePipe): cdef readonly TransitionSystem moves cdef public object _multitasks - cdef void _parseC(self, CBlas cblas, StateC** states, - WeightsC weights, SizesC sizes) nogil + cdef void _parseC( + self, + CBlas cblas, + StateC** states, + WeightsC weights, + SizesC sizes + ) nogil - cdef void c_transition_batch(self, StateC** states, const float* scores, - int nr_class, int batch_size) nogil + cdef void c_transition_batch( + self, + StateC** states, + const float* scores, + int nr_class, + int batch_size + ) nogil diff --git a/spacy/pipeline/transition_parser.pyx b/spacy/pipeline/transition_parser.pyx index ef4d9b362..11c8fafc7 100644 --- a/spacy/pipeline/transition_parser.pyx +++ b/spacy/pipeline/transition_parser.pyx @@ -7,20 +7,15 @@ from cymem.cymem cimport Pool from itertools import islice from libc.stdlib cimport calloc, free -from libc.string cimport memcpy, memset +from libc.string cimport memset from libcpp.vector cimport vector import random -import srsly -from thinc.api import CupyOps, NumpyOps, get_ops, set_dropout_rate - -from thinc.extra.search cimport Beam - -import warnings - import numpy import numpy.random +import srsly +from thinc.api import CupyOps, NumpyOps, set_dropout_rate from ..ml.parser_model cimport ( ActivationsC, @@ -42,7 +37,7 @@ from .trainable_pipe import TrainablePipe from ._parser_internals cimport _beam_utils from .. import util -from ..errors import Errors, Warnings +from ..errors import Errors from ..training import validate_examples, validate_get_examples from ._parser_internals import _beam_utils @@ -258,7 +253,6 @@ cdef class Parser(TrainablePipe): except Exception as e: error_handler(self.name, self, batch_in_order, e) - def predict(self, docs): if isinstance(docs, Doc): docs = [docs] @@ -300,8 +294,6 @@ cdef class Parser(TrainablePipe): return batch def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.): - cdef Beam beam - cdef Doc doc self._ensure_labels_are_added(docs) batch = _beam_utils.BeamBatch( self.moves, @@ -321,16 +313,18 @@ cdef class Parser(TrainablePipe): del model return list(batch) - cdef void _parseC(self, CBlas cblas, StateC** states, - WeightsC weights, SizesC sizes) nogil: - cdef int i, j + cdef void _parseC( + self, CBlas cblas, StateC** states, WeightsC weights, SizesC sizes + ) nogil: + cdef int i cdef vector[StateC*] unfinished cdef ActivationsC activations = alloc_activations(sizes) while sizes.states >= 1: predict_states(cblas, &activations, states, &weights, sizes) # Validate actions, argmax, take action. - self.c_transition_batch(states, - activations.scores, sizes.classes, sizes.states) + self.c_transition_batch( + states, activations.scores, sizes.classes, sizes.states + ) for i in range(sizes.states): if not states[i].is_final(): unfinished.push_back(states[i]) @@ -342,7 +336,6 @@ cdef class Parser(TrainablePipe): def set_annotations(self, docs, states_or_beams): cdef StateClass state - cdef Beam beam cdef Doc doc states = _beam_utils.collect_states(states_or_beams, docs) for i, (state, doc) in enumerate(zip(states, docs)): @@ -359,8 +352,13 @@ cdef class Parser(TrainablePipe): self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0]) return [state for state in states if not state.c.is_final()] - cdef void c_transition_batch(self, StateC** states, const float* scores, - int nr_class, int batch_size) nogil: + cdef void c_transition_batch( + self, + StateC** states, + const float* scores, + int nr_class, + int batch_size + ) nogil: # n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc with gil: assert self.moves.n_moves > 0, Errors.E924.format(name=self.name) @@ -380,7 +378,6 @@ cdef class Parser(TrainablePipe): free(is_valid) def update(self, examples, *, drop=0., sgd=None, losses=None): - cdef StateClass state if losses is None: losses = {} losses.setdefault(self.name, 0.) @@ -419,8 +416,7 @@ cdef class Parser(TrainablePipe): if not states: return losses model, backprop_tok2vec = self.model.begin_update([eg.x for eg in examples]) - - all_states = list(states) + states_golds = list(zip(states, golds)) n_moves = 0 while states_golds: @@ -500,8 +496,16 @@ cdef class Parser(TrainablePipe): del tutor return losses - def update_beam(self, examples, *, beam_width, - drop=0., sgd=None, losses=None, beam_density=0.0): + def update_beam( + self, + examples, + *, + beam_width, + drop=0., + sgd=None, + losses=None, + beam_density=0.0 + ): states, golds, _ = self.moves.init_gold_batch(examples) if not states: return losses @@ -531,8 +535,9 @@ cdef class Parser(TrainablePipe): is_valid = mem.alloc(self.moves.n_moves, sizeof(int)) costs = mem.alloc(self.moves.n_moves, sizeof(float)) - cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves), - dtype='f', order='C') + cdef np.ndarray d_scores = numpy.zeros( + (len(states), self.moves.n_moves), dtype='f', order='C' + ) c_d_scores = d_scores.data unseen_classes = self.model.attrs["unseen_classes"] for i, (state, gold) in enumerate(zip(states, golds)): @@ -542,8 +547,9 @@ cdef class Parser(TrainablePipe): for j in range(self.moves.n_moves): if costs[j] <= 0.0 and j in unseen_classes: unseen_classes.remove(j) - cpu_log_loss(c_d_scores, - costs, is_valid, &scores[i, 0], d_scores.shape[1]) + cpu_log_loss( + c_d_scores, costs, is_valid, &scores[i, 0], d_scores.shape[1] + ) c_d_scores += d_scores.shape[1] # Note that we don't normalize this. See comment in update() for why. if losses is not None: diff --git a/spacy/strings.pyx b/spacy/strings.pyx index 16c3e2b5b..b0799d6fc 100644 --- a/spacy/strings.pyx +++ b/spacy/strings.pyx @@ -2,7 +2,6 @@ cimport cython from libc.stdint cimport uint32_t from libc.string cimport memcpy -from libcpp.set cimport set from murmurhash.mrmr cimport hash32, hash64 import srsly @@ -20,9 +19,10 @@ cdef inline bint _try_coerce_to_hash(object key, hash_t* out_hash): try: out_hash[0] = key return True - except: + except: # no-cython-lint return False + def get_string_id(key): """Get a string ID, handling the reserved symbols correctly. If the key is already an ID, return it. @@ -87,7 +87,6 @@ cdef Utf8Str* _allocate(Pool mem, const unsigned char* chars, uint32_t length) e cdef int n_length_bytes cdef int i cdef Utf8Str* string = mem.alloc(1, sizeof(Utf8Str)) - cdef uint32_t ulength = length if length < sizeof(string.s): string.s[0] = length memcpy(&string.s[1], chars, length) diff --git a/spacy/structs.pxd b/spacy/structs.pxd index 9efb068fd..8cfcc2964 100644 --- a/spacy/structs.pxd +++ b/spacy/structs.pxd @@ -52,7 +52,7 @@ cdef struct TokenC: int sent_start int ent_iob - attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth.. + attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth.. attr_t ent_kb_id hash_t ent_id diff --git a/spacy/symbols.pxd b/spacy/symbols.pxd index bc15d9b80..73be19145 100644 --- a/spacy/symbols.pxd +++ b/spacy/symbols.pxd @@ -92,7 +92,7 @@ cdef enum symbol_t: ADV AUX CONJ - CCONJ # U20 + CCONJ # U20 DET INTJ NOUN @@ -418,7 +418,7 @@ cdef enum symbol_t: ccomp complm conj - cop # U20 + cop # U20 csubj csubjpass dep @@ -441,8 +441,8 @@ cdef enum symbol_t: num number oprd - obj # U20 - obl # U20 + obj # U20 + obl # U20 parataxis partmod pcomp diff --git a/spacy/symbols.pyx b/spacy/symbols.pyx index b0345c710..d1deeb0e7 100644 --- a/spacy/symbols.pyx +++ b/spacy/symbols.pyx @@ -96,7 +96,7 @@ IDS = { "ADV": ADV, "AUX": AUX, "CONJ": CONJ, - "CCONJ": CCONJ, # U20 + "CCONJ": CCONJ, # U20 "DET": DET, "INTJ": INTJ, "NOUN": NOUN, @@ -421,7 +421,7 @@ IDS = { "ccomp": ccomp, "complm": complm, "conj": conj, - "cop": cop, # U20 + "cop": cop, # U20 "csubj": csubj, "csubjpass": csubjpass, "dep": dep, @@ -444,8 +444,8 @@ IDS = { "num": num, "number": number, "oprd": oprd, - "obj": obj, # U20 - "obl": obl, # U20 + "obj": obj, # U20 + "obl": obl, # U20 "parataxis": parataxis, "partmod": partmod, "pcomp": pcomp, diff --git a/spacy/tests/package/test_requirements.py b/spacy/tests/package/test_requirements.py index 9e83d5fb1..fab1e8218 100644 --- a/spacy/tests/package/test_requirements.py +++ b/spacy/tests/package/test_requirements.py @@ -12,6 +12,7 @@ def test_build_dependencies(): "flake8", "hypothesis", "pre-commit", + "cython-lint", "black", "isort", "mypy", diff --git a/spacy/tokenizer.pxd b/spacy/tokenizer.pxd index f7585b45a..a902ebad9 100644 --- a/spacy/tokenizer.pxd +++ b/spacy/tokenizer.pxd @@ -31,24 +31,58 @@ cdef class Tokenizer: cdef Doc _tokenize_affixes(self, str string, bint with_special_cases) cdef int _apply_special_cases(self, Doc doc) except -1 - cdef void _filter_special_spans(self, vector[SpanC] &original, - vector[SpanC] &filtered, int doc_len) nogil - cdef object _prepare_special_spans(self, Doc doc, - vector[SpanC] &filtered) - cdef int _retokenize_special_spans(self, Doc doc, TokenC* tokens, - object span_data) - cdef int _try_specials_and_cache(self, hash_t key, Doc tokens, - int* has_special, - bint with_special_cases) except -1 - cdef int _tokenize(self, Doc tokens, str span, hash_t key, - int* has_special, bint with_special_cases) except -1 - cdef str _split_affixes(self, Pool mem, str string, - vector[LexemeC*] *prefixes, - vector[LexemeC*] *suffixes, int* has_special, - bint with_special_cases) - cdef int _attach_tokens(self, Doc tokens, str string, - vector[LexemeC*] *prefixes, - vector[LexemeC*] *suffixes, int* has_special, - bint with_special_cases) except -1 - cdef int _save_cached(self, const TokenC* tokens, hash_t key, - int* has_special, int n) except -1 + cdef void _filter_special_spans( + self, + vector[SpanC] &original, + vector[SpanC] &filtered, + int doc_len, + ) nogil + cdef object _prepare_special_spans( + self, + Doc doc, + vector[SpanC] &filtered, + ) + cdef int _retokenize_special_spans( + self, + Doc doc, + TokenC* tokens, + object span_data, + ) + cdef int _try_specials_and_cache( + self, + hash_t key, + Doc tokens, + int* has_special, + bint with_special_cases, + ) except -1 + cdef int _tokenize( + self, + Doc tokens, + str span, + hash_t key, + int* has_special, + bint with_special_cases, + ) except -1 + cdef str _split_affixes( + self, + Pool mem, + str string, + vector[LexemeC*] *prefixes, + vector[LexemeC*] *suffixes, int* has_special, + bint with_special_cases, + ) + cdef int _attach_tokens( + self, + Doc tokens, + str string, + vector[LexemeC*] *prefixes, + vector[LexemeC*] *suffixes, int* has_special, + bint with_special_cases, + ) except -1 + cdef int _save_cached( + self, + const TokenC* tokens, + hash_t key, + int* has_special, + int n, + ) except -1 diff --git a/spacy/tokenizer.pyx b/spacy/tokenizer.pyx index 3861b1cee..8fc95bea0 100644 --- a/spacy/tokenizer.pyx +++ b/spacy/tokenizer.pyx @@ -8,20 +8,18 @@ from libcpp.set cimport set as stdset from preshed.maps cimport PreshMap import re -import warnings - from .lexeme cimport EMPTY_LEXEME from .strings cimport hash_string from .tokens.doc cimport Doc from . import util from .attrs import intify_attrs -from .errors import Errors, Warnings +from .errors import Errors from .scorer import Scorer from .symbols import NORM, ORTH from .tokens import Span from .training import validate_examples -from .util import get_words_and_spaces, registry +from .util import get_words_and_spaces cdef class Tokenizer: @@ -324,7 +322,7 @@ cdef class Tokenizer: cdef int span_start cdef int span_end while i < doc.length: - if not i in span_data: + if i not in span_data: tokens[i + offset] = doc.c[i] i += 1 else: @@ -395,12 +393,15 @@ cdef class Tokenizer: self._save_cached(&tokens.c[orig_size], orig_key, has_special, tokens.length - orig_size) - cdef str _split_affixes(self, Pool mem, str string, - vector[const LexemeC*] *prefixes, - vector[const LexemeC*] *suffixes, - int* has_special, - bint with_special_cases): - cdef size_t i + cdef str _split_affixes( + self, + Pool mem, + str string, + vector[const LexemeC*] *prefixes, + vector[const LexemeC*] *suffixes, + int* has_special, + bint with_special_cases + ): cdef str prefix cdef str suffix cdef str minus_pre @@ -445,10 +446,6 @@ cdef class Tokenizer: vector[const LexemeC*] *suffixes, int* has_special, bint with_special_cases) except -1: - cdef bint specials_hit = 0 - cdef bint cache_hit = 0 - cdef int split, end - cdef const LexemeC* const* lexemes cdef const LexemeC* lexeme cdef str span cdef int i @@ -458,9 +455,11 @@ cdef class Tokenizer: if string: if self._try_specials_and_cache(hash_string(string), tokens, has_special, with_special_cases): pass - elif (self.token_match and self.token_match(string)) or \ - (self.url_match and \ - self.url_match(string)): + elif ( + (self.token_match and self.token_match(string)) or + (self.url_match and self.url_match(string)) + ): + # We're always saying 'no' to spaces here -- the caller will # fix up the outermost one, with reference to the original. # See Issue #859 @@ -821,7 +820,7 @@ cdef class Tokenizer: self.infix_finditer = None self.token_match = None self.url_match = None - msg = util.from_bytes(bytes_data, deserializers, exclude) + util.from_bytes(bytes_data, deserializers, exclude) if "prefix_search" in data and isinstance(data["prefix_search"], str): self.prefix_search = re.compile(data["prefix_search"]).search if "suffix_search" in data and isinstance(data["suffix_search"], str): diff --git a/spacy/tokens/_retokenize.pyx b/spacy/tokens/_retokenize.pyx index 8ed707ab9..f28d2e088 100644 --- a/spacy/tokens/_retokenize.pyx +++ b/spacy/tokens/_retokenize.pyx @@ -1,7 +1,6 @@ # cython: infer_types=True, bounds_check=False, profile=True from cymem.cymem cimport Pool -from libc.stdlib cimport free, malloc -from libc.string cimport memcpy, memset +from libc.string cimport memset import numpy from thinc.api import get_array_module @@ -10,7 +9,7 @@ from ..attrs cimport MORPH, NORM from ..lexeme cimport EMPTY_LEXEME, Lexeme from ..structs cimport LexemeC, TokenC from ..vocab cimport Vocab -from .doc cimport Doc, set_children_from_heads, token_by_end, token_by_start +from .doc cimport Doc, set_children_from_heads, token_by_start from .span cimport Span from .token cimport Token @@ -147,7 +146,7 @@ def _merge(Doc doc, merges): syntactic root of the span. RETURNS (Token): The first newly merged token. """ - cdef int i, merge_index, start, end, token_index, current_span_index, current_offset, offset, span_index + cdef int i, merge_index, start, token_index, current_span_index, current_offset, offset, span_index cdef Span span cdef const LexemeC* lex cdef TokenC* token @@ -165,7 +164,6 @@ def _merge(Doc doc, merges): merges.sort(key=_get_start) for merge_index, (span, attributes) in enumerate(merges): start = span.start - end = span.end spans.append(span) # House the new merged token where it starts token = &doc.c[start] @@ -203,8 +201,9 @@ def _merge(Doc doc, merges): # for the merged region. To do this, we create a boolean array indicating # whether the row is to be deleted, then use numpy.delete if doc.tensor is not None and doc.tensor.size != 0: - doc.tensor = _resize_tensor(doc.tensor, - [(m[0].start, m[0].end) for m in merges]) + doc.tensor = _resize_tensor( + doc.tensor, [(m[0].start, m[0].end) for m in merges] + ) # Memorize span roots and sets dependencies of the newly merged # tokens to the dependencies of their roots. span_roots = [] @@ -267,11 +266,11 @@ def _merge(Doc doc, merges): span_index += 1 if span_index < len(spans) and i == spans[span_index].start: # First token in a span - doc.c[i - offset] = doc.c[i] # move token to its place + doc.c[i - offset] = doc.c[i] # move token to its place offset += (spans[span_index].end - spans[span_index].start) - 1 in_span = True if not in_span: - doc.c[i - offset] = doc.c[i] # move token to its place + doc.c[i - offset] = doc.c[i] # move token to its place for i in range(doc.length - offset, doc.length): memset(&doc.c[i], 0, sizeof(TokenC)) @@ -345,7 +344,11 @@ def _split(Doc doc, int token_index, orths, heads, attrs): if to_process_tensor: xp = get_array_module(doc.tensor) if xp is numpy: - doc.tensor = xp.append(doc.tensor, xp.zeros((nb_subtokens,doc.tensor.shape[1]), dtype="float32"), axis=0) + doc.tensor = xp.append( + doc.tensor, + xp.zeros((nb_subtokens, doc.tensor.shape[1]), dtype="float32"), + axis=0 + ) else: shape = (doc.tensor.shape[0] + nb_subtokens, doc.tensor.shape[1]) resized_array = xp.zeros(shape, dtype="float32") @@ -367,7 +370,8 @@ def _split(Doc doc, int token_index, orths, heads, attrs): token.norm = 0 # reset norm if to_process_tensor: # setting the tensors of the split tokens to array of zeros - doc.tensor[token_index + i:token_index + i + 1] = xp.zeros((1,doc.tensor.shape[1]), dtype="float32") + doc.tensor[token_index + i:token_index + i + 1] = \ + xp.zeros((1, doc.tensor.shape[1]), dtype="float32") # Update the character offset of the subtokens if i != 0: token.idx = orig_token.idx + idx_offset @@ -455,7 +459,6 @@ def normalize_token_attrs(Vocab vocab, attrs): def set_token_attrs(Token py_token, attrs): cdef TokenC* token = py_token.c cdef const LexemeC* lex = token.lex - cdef Doc doc = py_token.doc # Assign attributes for attr_name, attr_value in attrs.items(): if attr_name == "_": # Set extension attributes diff --git a/spacy/tokens/doc.pxd b/spacy/tokens/doc.pxd index d7f092c94..d9719609c 100644 --- a/spacy/tokens/doc.pxd +++ b/spacy/tokens/doc.pxd @@ -31,7 +31,7 @@ cdef int token_by_start(const TokenC* tokens, int length, int start_char) except cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2 -cdef int [:,:] _get_lca_matrix(Doc, int start, int end) +cdef int [:, :] _get_lca_matrix(Doc, int start, int end) cdef class Doc: @@ -61,7 +61,6 @@ cdef class Doc: cdef int length cdef int max_length - cdef public object noun_chunks_iterator cdef object __weakref__ diff --git a/spacy/tokens/doc.pyx b/spacy/tokens/doc.pyx index 146b276e2..8fc2c4b3c 100644 --- a/spacy/tokens/doc.pyx +++ b/spacy/tokens/doc.pyx @@ -43,14 +43,13 @@ from ..attrs cimport ( attr_id_t, ) from ..lexeme cimport EMPTY_LEXEME, Lexeme -from ..typedefs cimport attr_t, flags_t +from ..typedefs cimport attr_t from .token cimport Token from .. import parts_of_speech, schemas, util from ..attrs import IDS, intify_attr -from ..compat import copy_reg, pickle +from ..compat import copy_reg from ..errors import Errors, Warnings -from ..morphology import Morphology from ..util import get_words_and_spaces from ._retokenize import Retokenizer from .underscore import Underscore, get_ext_args @@ -784,7 +783,7 @@ cdef class Doc: # TODO: # 1. Test basic data-driven ORTH gazetteer # 2. Test more nuanced date and currency regex - cdef attr_t entity_type, kb_id, ent_id + cdef attr_t kb_id, ent_id cdef int ent_start, ent_end ent_spans = [] for ent_info in ents: @@ -987,7 +986,6 @@ cdef class Doc: >>> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA]) """ cdef int i, j - cdef attr_id_t feature cdef np.ndarray[attr_t, ndim=2] output # Handle scalar/list inputs of strings/ints for py_attr_ids # See also #3064 @@ -999,8 +997,10 @@ cdef class Doc: py_attr_ids = [py_attr_ids] # Allow strings, e.g. 'lemma' or 'LEMMA' try: - py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_) - for id_ in py_attr_ids] + py_attr_ids = [ + (IDS[id_.upper()] if hasattr(id_, "upper") else id_) + for id_ in py_attr_ids + ] except KeyError as msg: keys = [k for k in IDS.keys() if not k.startswith("FLAG")] raise KeyError(Errors.E983.format(dict="IDS", key=msg, keys=keys)) from None @@ -1030,8 +1030,6 @@ cdef class Doc: DOCS: https://spacy.io/api/doc#count_by """ cdef int i - cdef attr_t attr - cdef size_t count if counts is None: counts = Counter() @@ -1093,7 +1091,6 @@ cdef class Doc: cdef int i, col cdef int32_t abs_head_index cdef attr_id_t attr_id - cdef TokenC* tokens = self.c cdef int length = len(array) if length != len(self): raise ValueError(Errors.E971.format(array_length=length, doc_length=len(self))) @@ -1225,7 +1222,7 @@ cdef class Doc: span.label, span.kb_id, span.id, - span.text, # included as a check + span.text, # included as a check )) char_offset += len(doc.text) if len(doc) > 0 and ensure_whitespace and not doc[-1].is_space and not bool(doc[-1].whitespace_): @@ -1508,7 +1505,6 @@ cdef class Doc: attributes are inherited from the syntactic root of the span. RETURNS (Token): The first newly merged token. """ - cdef str tag, lemma, ent_type attr_len = len(attributes) span_len = len(spans) if not attr_len == span_len: @@ -1624,7 +1620,6 @@ cdef class Doc: for token in char_span[1:]: token.is_sent_start = False - for span_group in doc_json.get("spans", {}): spans = [] for span in doc_json["spans"][span_group]: @@ -1656,7 +1651,7 @@ cdef class Doc: start = token_by_char(self.c, self.length, token_data["start"]) value = token_data["value"] self[start]._.set(token_attr, value) - + for span_attr in doc_json.get("underscore_span", {}): if not Span.has_extension(span_attr): Span.set_extension(span_attr) @@ -1698,7 +1693,7 @@ cdef class Doc: token_data["dep"] = token.dep_ token_data["head"] = token.head.i data["tokens"].append(token_data) - + if self.spans: data["spans"] = {} for span_group in self.spans: @@ -1769,7 +1764,6 @@ cdef class Doc: output.fill(255) cdef int i, j, start_idx, end_idx cdef bytes byte_string - cdef unsigned char utf8_char for i, byte_string in enumerate(byte_strings): j = 0 start_idx = 0 @@ -1822,8 +1816,6 @@ cdef int token_by_char(const TokenC* tokens, int length, int char_idx) except -2 cdef int set_children_from_heads(TokenC* tokens, int start, int end) except -1: # note: end is exclusive - cdef TokenC* head - cdef TokenC* child cdef int i # Set number of left/right children to 0. We'll increment it in the loops. for i in range(start, end): @@ -1923,7 +1915,7 @@ cdef int _get_tokens_lca(Token token_j, Token token_k): return -1 -cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end): +cdef int [:, :] _get_lca_matrix(Doc doc, int start, int end): """Given a doc and a start and end position defining a set of contiguous tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where LCA[i, j] is the index of the lowest common ancestor among token i and j. @@ -1936,7 +1928,7 @@ cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end): RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32], with shape (n, n), where n = len(doc). """ - cdef int [:,:] lca_matrix + cdef int [:, :] lca_matrix cdef int j, k n_tokens= end - start lca_mat = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32) diff --git a/spacy/tokens/graph.pyx b/spacy/tokens/graph.pyx index 47f0a20d4..1cbec09f4 100644 --- a/spacy/tokens/graph.pyx +++ b/spacy/tokens/graph.pyx @@ -3,7 +3,7 @@ from typing import Generator, List, Tuple cimport cython from cython.operator cimport dereference -from libc.stdint cimport int32_t, int64_t +from libc.stdint cimport int32_t from libcpp.pair cimport pair from libcpp.unordered_map cimport unordered_map from libcpp.unordered_set cimport unordered_set @@ -11,7 +11,6 @@ from libcpp.unordered_set cimport unordered_set import weakref from murmurhash.mrmr cimport hash64 -from preshed.maps cimport map_get_unless_missing from .. import Errors @@ -28,7 +27,7 @@ from .token import Token cdef class Edge: cdef readonly Graph graph cdef readonly int i - + def __init__(self, Graph graph, int i): self.graph = graph self.i = i @@ -44,7 +43,7 @@ cdef class Edge: @property def head(self) -> "Node": return Node(self.graph, self.graph.c.edges[self.i].head) - + @property def tail(self) -> "Tail": return Node(self.graph, self.graph.c.edges[self.i].tail) @@ -70,7 +69,7 @@ cdef class Node: def __init__(self, Graph graph, int i): """A reference to a node of an annotation graph. Each node is made up of an ordered set of zero or more token indices. - + Node references are usually created by the Graph object itself, or from the Node or Edge objects. You usually won't need to instantiate this class yourself. @@ -109,13 +108,13 @@ cdef class Node: @property def is_none(self) -> bool: """Whether the node is a special value, indicating 'none'. - + The NoneNode type is returned by the Graph, Edge and Node objects when there is no match to a query. It has the same API as Node, but it always returns NoneNode, NoneEdge or empty lists for its queries. """ return False - + @property def doc(self) -> "Doc": """The Doc object that the graph refers to.""" @@ -130,19 +129,19 @@ cdef class Node: def head(self, i=None, label=None) -> "Node": """Get the head of the first matching edge, searching by index, label, both or neither. - + For instance, `node.head(i=1)` will get the head of the second edge that this node is a tail of. `node.head(i=1, label="ARG0")` will further check that the second edge has the label `"ARG0"`. - + If no matching node can be found, the graph's NoneNode is returned. """ return self.headed(i=i, label=label) - + def tail(self, i=None, label=None) -> "Node": """Get the tail of the first matching edge, searching by index, label, both or neither. - + If no matching node can be found, the graph's NoneNode is returned. """ return self.tailed(i=i, label=label).tail @@ -171,7 +170,7 @@ cdef class Node: cdef vector[int] edge_indices self._find_edges(edge_indices, "head", label) return [Node(self.graph, self.graph.c.edges[i].head) for i in edge_indices] - + def tails(self, label=None) -> List["Node"]: """Find all matching tails of this node.""" cdef vector[int] edge_indices @@ -200,7 +199,7 @@ cdef class Node: return NoneEdge(self.graph) else: return Edge(self.graph, idx) - + def tailed(self, i=None, label=None) -> Edge: """Find the first matching edge tailed by this node. If no matching edge can be found, the graph's NoneEdge is returned. @@ -283,7 +282,7 @@ cdef class NoneEdge(Edge): def __init__(self, graph): self.graph = graph self.i = -1 - + @property def doc(self) -> "Doc": return self.graph.doc @@ -291,7 +290,7 @@ cdef class NoneEdge(Edge): @property def head(self) -> "NoneNode": return NoneNode(self.graph) - + @property def tail(self) -> "NoneNode": return NoneNode(self.graph) @@ -319,7 +318,7 @@ cdef class NoneNode(Node): def __len__(self): return 0 - + @property def is_none(self): return -1 @@ -340,14 +339,14 @@ cdef class NoneNode(Node): def walk_heads(self): yield from [] - + def walk_tails(self): yield from [] - + cdef class Graph: """A set of directed labelled relationships between sets of tokens. - + EXAMPLE: Construction 1 >>> graph = Graph(doc, name="srl") @@ -372,7 +371,9 @@ cdef class Graph: >>> assert graph.has_node((0,)) >>> assert graph.has_edge((0,), (1,3), label="agent") """ - def __init__(self, doc, *, name="", nodes=[], edges=[], labels=None, weights=None): + def __init__( + self, doc, *, name="", nodes=[], edges=[], labels=None, weights=None # no-cython-lint + ): """Create a Graph object. doc (Doc): The Doc object the graph will refer to. @@ -438,13 +439,11 @@ cdef class Graph: def add_edge(self, head, tail, *, label="", weight=None) -> Edge: """Add an edge to the graph, connecting two groups of tokens. - + If there is already an edge for the (head, tail, label) triple, it will be returned, and no new edge will be created. The weight of the edge will be updated if a weight is specified. """ - label_hash = self.doc.vocab.strings.as_int(label) - weight_float = weight if weight is not None else 0.0 edge_index = add_edge( &self.c, EdgeC( @@ -478,11 +477,11 @@ cdef class Graph: def has_edge(self, head, tail, label) -> bool: """Check whether a (head, tail, label) triple is an edge in the graph.""" return not self.get_edge(head, tail, label=label).is_none - + def add_node(self, indices) -> Node: """Add a node to the graph and return it. Nodes refer to ordered sets of token indices. - + This method is idempotent: if there is already a node for the given indices, it is returned without a new node being created. """ @@ -510,7 +509,7 @@ cdef class Graph: return NoneNode(self) else: return Node(self, node_index) - + def has_node(self, tuple indices) -> bool: """Check whether the graph has a node for the given indices.""" return not self.get_node(indices).is_none @@ -570,7 +569,7 @@ cdef int add_node(GraphC* graph, vector[int32_t]& node) nogil: graph.roots.insert(index) graph.node_map.insert(pair[hash_t, int](key, index)) return index - + cdef int get_node(const GraphC* graph, vector[int32_t] node) nogil: key = hash64(&node[0], node.size() * sizeof(node[0]), 0) diff --git a/spacy/tokens/morphanalysis.pyx b/spacy/tokens/morphanalysis.pyx index 0992a0b66..ba7c638f6 100644 --- a/spacy/tokens/morphanalysis.pyx +++ b/spacy/tokens/morphanalysis.pyx @@ -89,4 +89,3 @@ cdef class MorphAnalysis: def __repr__(self): return self.to_json() - diff --git a/spacy/tokens/span.pyx b/spacy/tokens/span.pyx index 59ee21687..cf90e416b 100644 --- a/spacy/tokens/span.pyx +++ b/spacy/tokens/span.pyx @@ -1,5 +1,4 @@ cimport numpy as np -from libc.math cimport sqrt import copy import warnings @@ -10,11 +9,10 @@ from thinc.api import get_array_module from ..attrs cimport * from ..attrs cimport ORTH, attr_id_t from ..lexeme cimport Lexeme -from ..parts_of_speech cimport univ_pos_t -from ..structs cimport LexemeC, TokenC +from ..structs cimport TokenC from ..symbols cimport dep -from ..typedefs cimport attr_t, flags_t, hash_t -from .doc cimport _get_lca_matrix, get_token_attr, token_by_end, token_by_start +from ..typedefs cimport attr_t, hash_t +from .doc cimport _get_lca_matrix, get_token_attr from .token cimport Token from ..errors import Errors, Warnings @@ -595,7 +593,6 @@ cdef class Span: """ return "".join([t.text_with_ws for t in self]) - @property def noun_chunks(self): """Iterate over the base noun phrases in the span. Yields base diff --git a/spacy/tokens/span_group.pyx b/spacy/tokens/span_group.pyx index 48ad4a516..d245a1425 100644 --- a/spacy/tokens/span_group.pyx +++ b/spacy/tokens/span_group.pyx @@ -1,7 +1,7 @@ import struct import weakref from copy import deepcopy -from typing import TYPE_CHECKING, Iterable, Optional, Tuple, Union +from typing import Iterable, Optional, Union import srsly @@ -34,7 +34,7 @@ cdef class SpanGroup: DOCS: https://spacy.io/api/spangroup """ - def __init__(self, doc, *, name="", attrs={}, spans=[]): + def __init__(self, doc, *, name="", attrs={}, spans=[]): # no-cython-lint """Create a SpanGroup. doc (Doc): The reference Doc object. @@ -311,7 +311,7 @@ cdef class SpanGroup: other_attrs = deepcopy(other_group.attrs) span_group.attrs.update({ - key: value for key, value in other_attrs.items() \ + key: value for key, value in other_attrs.items() if key not in span_group.attrs }) if len(other_group): diff --git a/spacy/tokens/token.pxd b/spacy/tokens/token.pxd index fc02ff624..f4e4611df 100644 --- a/spacy/tokens/token.pxd +++ b/spacy/tokens/token.pxd @@ -26,7 +26,7 @@ cdef class Token: cdef Token self = Token.__new__(Token, vocab, doc, offset) return self - #cdef inline TokenC struct_from_attrs(Vocab vocab, attrs): + # cdef inline TokenC struct_from_attrs(Vocab vocab, attrs): # cdef TokenC token # attrs = normalize_attrs(attrs) @@ -98,12 +98,10 @@ cdef class Token: elif feat_name == SENT_START: token.sent_start = value - @staticmethod cdef inline int missing_dep(const TokenC* token) nogil: return token.dep == MISSING_DEP - @staticmethod cdef inline int missing_head(const TokenC* token) nogil: return Token.missing_dep(token) diff --git a/spacy/tokens/token.pyx b/spacy/tokens/token.pyx index 6018c3112..de967ba25 100644 --- a/spacy/tokens/token.pyx +++ b/spacy/tokens/token.pyx @@ -1,13 +1,11 @@ # cython: infer_types=True # Compiler crashes on memory view coercion without this. Should report bug. cimport numpy as np -from cython.view cimport array as cvarray np.import_array() import warnings -import numpy from thinc.api import get_array_module from ..attrs cimport ( @@ -238,7 +236,7 @@ cdef class Token: result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm) # ensure we get a scalar back (numpy does this automatically but cupy doesn't) return result.item() - + def has_morph(self): """Check whether the token has annotated morph information. Return False when the morph annotation is unset/missing. @@ -545,9 +543,9 @@ cdef class Token: def __get__(self): if self.i + 1 == len(self.doc): return True - elif self.doc[self.i+1].is_sent_start == None: + elif self.doc[self.i+1].is_sent_start is None: return None - elif self.doc[self.i+1].is_sent_start == True: + elif self.doc[self.i+1].is_sent_start is True: return True else: return False diff --git a/spacy/training/align.pyx b/spacy/training/align.pyx index 8bd43b048..79fec73c4 100644 --- a/spacy/training/align.pyx +++ b/spacy/training/align.pyx @@ -37,10 +37,14 @@ def get_alignments(A: List[str], B: List[str]) -> Tuple[List[List[int]], List[Li b2a.append(set()) # Process the alignment at the current position if A[token_idx_a] == B[token_idx_b] and \ - (char_idx_a == 0 or \ - char_to_token_a[char_idx_a - 1] < token_idx_a) and \ - (char_idx_b == 0 or \ - char_to_token_b[char_idx_b - 1] < token_idx_b): + ( + char_idx_a == 0 or + char_to_token_a[char_idx_a - 1] < token_idx_a + ) and \ + ( + char_idx_b == 0 or + char_to_token_b[char_idx_b - 1] < token_idx_b + ): # Current tokens are identical and both character offsets are the # start of a token (either at the beginning of the document or the # previous character belongs to a different token) diff --git a/spacy/training/example.pyx b/spacy/training/example.pyx index abdac23ea..3f0cf5ade 100644 --- a/spacy/training/example.pyx +++ b/spacy/training/example.pyx @@ -1,4 +1,3 @@ -import warnings from collections.abc import Iterable as IterableInstance import numpy @@ -31,9 +30,9 @@ cpdef Doc annotations_to_doc(vocab, tok_annot, doc_annot): attrs, array = _annot2array(vocab, tok_annot, doc_annot) output = Doc(vocab, words=tok_annot["ORTH"], spaces=tok_annot["SPACY"]) if "entities" in doc_annot: - _add_entities_to_doc(output, doc_annot["entities"]) + _add_entities_to_doc(output, doc_annot["entities"]) if "spans" in doc_annot: - _add_spans_to_doc(output, doc_annot["spans"]) + _add_spans_to_doc(output, doc_annot["spans"]) if array.size: output = output.from_array(attrs, array) # links are currently added with ENT_KB_ID on the token level @@ -161,7 +160,6 @@ cdef class Example: self._y_sig = y_sig return self._cached_alignment - def _get_aligned_vectorized(self, align, gold_values): # Fast path for Doc attributes/fields that are predominantly a single value, # i.e., TAG, POS, MORPH. @@ -204,7 +202,6 @@ cdef class Example: return output.tolist() - def _get_aligned_non_vectorized(self, align, gold_values): # Slower path for fields that return multiple values (resulting # in ragged arrays that cannot be vectorized trivially). @@ -221,7 +218,6 @@ cdef class Example: return output - def get_aligned(self, field, as_string=False): """Return an aligned array for a token attribute.""" align = self.alignment.x2y @@ -330,7 +326,7 @@ cdef class Example: missing=None ) # Now fill the tokens we can align to O. - O = 2 # I=1, O=2, B=3 + O = 2 # I=1, O=2, B=3 # no-cython-lint: E741 for i, ent_iob in enumerate(self.get_aligned("ENT_IOB")): if x_tags[i] is None: if ent_iob == O: @@ -340,7 +336,7 @@ cdef class Example: return x_ents, x_tags def get_aligned_ner(self): - x_ents, x_tags = self.get_aligned_ents_and_ner() + _x_ents, x_tags = self.get_aligned_ents_and_ner() return x_tags def get_matching_ents(self, check_label=True): @@ -398,7 +394,6 @@ cdef class Example: return span_dict - def _links_to_dict(self): links = {} for ent in self.reference.ents: @@ -589,6 +584,7 @@ def _fix_legacy_dict_data(example_dict): "doc_annotation": doc_dict } + def _has_field(annot, field): if field not in annot: return False @@ -625,6 +621,7 @@ def _parse_ner_tags(biluo_or_offsets, vocab, words, spaces): ent_types.append("") return ent_iobs, ent_types + def _parse_links(vocab, words, spaces, links): reference = Doc(vocab, words=words, spaces=spaces) starts = {token.idx: token.i for token in reference} diff --git a/spacy/training/gold_io.pyx b/spacy/training/gold_io.pyx index 1e7b3681d..2fc36e41f 100644 --- a/spacy/training/gold_io.pyx +++ b/spacy/training/gold_io.pyx @@ -1,4 +1,3 @@ -import json import warnings import srsly @@ -6,7 +5,7 @@ import srsly from .. import util from ..errors import Warnings from ..tokens import Doc -from .iob_utils import offsets_to_biluo_tags, tags_to_entities +from .iob_utils import offsets_to_biluo_tags def docs_to_json(docs, doc_id=0, ner_missing_tag="O"): @@ -23,7 +22,13 @@ def docs_to_json(docs, doc_id=0, ner_missing_tag="O"): json_doc = {"id": doc_id, "paragraphs": []} for i, doc in enumerate(docs): raw = None if doc.has_unknown_spaces else doc.text - json_para = {'raw': raw, "sentences": [], "cats": [], "entities": [], "links": []} + json_para = { + 'raw': raw, + "sentences": [], + "cats": [], + "entities": [], + "links": [] + } for cat, val in doc.cats.items(): json_cat = {"label": cat, "value": val} json_para["cats"].append(json_cat) @@ -35,13 +40,17 @@ def docs_to_json(docs, doc_id=0, ner_missing_tag="O"): if ent.kb_id_: link_dict = {(ent.start_char, ent.end_char): {ent.kb_id_: 1.0}} json_para["links"].append(link_dict) - biluo_tags = offsets_to_biluo_tags(doc, json_para["entities"], missing=ner_missing_tag) + biluo_tags = offsets_to_biluo_tags( + doc, json_para["entities"], missing=ner_missing_tag + ) attrs = ("TAG", "POS", "MORPH", "LEMMA", "DEP", "ENT_IOB") include_annotation = {attr: doc.has_annotation(attr) for attr in attrs} for j, sent in enumerate(doc.sents): json_sent = {"tokens": [], "brackets": []} for token in sent: - json_token = {"id": token.i, "orth": token.text, "space": token.whitespace_} + json_token = { + "id": token.i, "orth": token.text, "space": token.whitespace_ + } if include_annotation["TAG"]: json_token["tag"] = token.tag_ if include_annotation["POS"]: @@ -125,9 +134,14 @@ def json_to_annotations(doc): else: sent_starts.append(-1) if "brackets" in sent: - brackets.extend((b["first"] + sent_start_i, - b["last"] + sent_start_i, b["label"]) - for b in sent["brackets"]) + brackets.extend( + ( + b["first"] + sent_start_i, + b["last"] + sent_start_i, + b["label"] + ) + for b in sent["brackets"] + ) example["token_annotation"] = dict( ids=ids, @@ -160,6 +174,7 @@ def json_to_annotations(doc): ) yield example + def json_iterate(bytes utf8_str): # We should've made these files jsonl...But since we didn't, parse out # the docs one-by-one to reduce memory usage. diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx index bf79481b8..a88f380f9 100644 --- a/spacy/vectors.pyx +++ b/spacy/vectors.pyx @@ -1,10 +1,8 @@ -cimport numpy as np from cython.operator cimport dereference as deref from libc.stdint cimport uint32_t, uint64_t from libcpp.set cimport set as cppset from murmurhash.mrmr cimport hash128_x64 -import functools import warnings from enum import Enum from typing import cast @@ -119,7 +117,7 @@ cdef class Vectors: if self.mode == Mode.default: if data is None: if shape is None: - shape = (0,0) + shape = (0, 0) ops = get_current_ops() data = ops.xp.zeros(shape, dtype="f") self._unset = cppset[int]({i for i in range(data.shape[0])}) @@ -260,11 +258,10 @@ cdef class Vectors: def __eq__(self, other): # Check for equality, with faster checks first return ( - self.shape == other.shape - and self.key2row == other.key2row - and self.to_bytes(exclude=["strings"]) - == other.to_bytes(exclude=["strings"]) - ) + self.shape == other.shape + and self.key2row == other.key2row + and self.to_bytes(exclude=["strings"]) == other.to_bytes(exclude=["strings"]) + ) def resize(self, shape, inplace=False): """Resize the underlying vectors array. If inplace=True, the memory @@ -520,11 +517,12 @@ cdef class Vectors: # vectors e.g. (10000, 300) # sims e.g. (1024, 10000) sims = xp.dot(batch, vectors.T) - best_rows[i:i+batch_size] = xp.argpartition(sims, -n, axis=1)[:,-n:] - scores[i:i+batch_size] = xp.partition(sims, -n, axis=1)[:,-n:] + best_rows[i:i+batch_size] = xp.argpartition(sims, -n, axis=1)[:, -n:] + scores[i:i+batch_size] = xp.partition(sims, -n, axis=1)[:, -n:] if sort and n >= 2: - sorted_index = xp.arange(scores.shape[0])[:,None][i:i+batch_size],xp.argsort(scores[i:i+batch_size], axis=1)[:,::-1] + sorted_index = xp.arange(scores.shape[0])[:, None][i:i+batch_size], \ + xp.argsort(scores[i:i+batch_size], axis=1)[:, ::-1] scores[i:i+batch_size] = scores[sorted_index] best_rows[i:i+batch_size] = best_rows[sorted_index] @@ -538,8 +536,12 @@ cdef class Vectors: numpy_rows = get_current_ops().to_numpy(best_rows) keys = xp.asarray( - [[row2key[row] for row in numpy_rows[i] if row in row2key] - for i in range(len(queries)) ], dtype="uint64") + [ + [row2key[row] for row in numpy_rows[i] if row in row2key] + for i in range(len(queries)) + ], + dtype="uint64" + ) return (keys, best_rows, scores) def to_ops(self, ops: Ops): @@ -582,9 +584,9 @@ cdef class Vectors: """ xp = get_array_module(self.data) if xp is numpy: - save_array = lambda arr, file_: xp.save(file_, arr, allow_pickle=False) + save_array = lambda arr, file_: xp.save(file_, arr, allow_pickle=False) # no-cython-lint else: - save_array = lambda arr, file_: xp.save(file_, arr) + save_array = lambda arr, file_: xp.save(file_, arr) # no-cython-lint def save_vectors(path): # the source of numpy.save indicates that the file object is closed after use. diff --git a/spacy/vocab.pxd b/spacy/vocab.pxd index 3b0173e3e..43e47af1d 100644 --- a/spacy/vocab.pxd +++ b/spacy/vocab.pxd @@ -32,7 +32,7 @@ cdef class Vocab: cdef public object writing_system cdef public object get_noun_chunks cdef readonly int length - cdef public object _unused_object # TODO remove in v4, see #9150 + cdef public object _unused_object # TODO remove in v4, see #9150 cdef public object lex_attr_getters cdef public object cfg diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx index 520228b51..d1edc8533 100644 --- a/spacy/vocab.pyx +++ b/spacy/vocab.pyx @@ -1,6 +1,4 @@ # cython: profile=True -from libc.string cimport memcpy - import functools import numpy @@ -19,7 +17,6 @@ from .errors import Errors from .lang.lex_attrs import LEX_ATTRS, get_lang, is_stop from .lang.norm_exceptions import BASE_NORMS from .lookups import Lookups -from .util import registry from .vectors import Mode as VectorsMode from .vectors import Vectors @@ -51,9 +48,17 @@ cdef class Vocab: DOCS: https://spacy.io/api/vocab """ - def __init__(self, lex_attr_getters=None, strings=tuple(), lookups=None, - oov_prob=-20., vectors_name=None, writing_system={}, - get_noun_chunks=None, **deprecated_kwargs): + def __init__( + self, + lex_attr_getters=None, + strings=tuple(), + lookups=None, + oov_prob=-20., + vectors_name=None, + writing_system={}, # no-cython-lint + get_noun_chunks=None, + **deprecated_kwargs + ): """Create the vocabulary. lex_attr_getters (dict): A dictionary mapping attribute IDs to @@ -150,7 +155,6 @@ cdef class Vocab: cdef LexemeC* lex cdef hash_t key = self.strings[string] lex = self._by_orth.get(key) - cdef size_t addr if lex != NULL: assert lex.orth in self.strings if lex.orth != key: @@ -183,7 +187,7 @@ cdef class Vocab: # of the doc ownership). # TODO: Change the C API so that the mem isn't passed in here. mem = self.mem - #if len(string) < 3 or self.length < 10000: + # if len(string) < 3 or self.length < 10000: # mem = self.mem cdef bint is_oov = mem is not self.mem lex = mem.alloc(1, sizeof(LexemeC)) @@ -463,7 +467,6 @@ cdef class Vocab: self.lookups.get_table("lexeme_norm"), ) - def to_disk(self, path, *, exclude=tuple()): """Save the current state to a directory. @@ -476,7 +479,6 @@ cdef class Vocab: path = util.ensure_path(path) if not path.exists(): path.mkdir() - setters = ["strings", "vectors"] if "strings" not in exclude: self.strings.to_disk(path / "strings.json") if "vectors" not in exclude: @@ -495,7 +497,6 @@ cdef class Vocab: DOCS: https://spacy.io/api/vocab#to_disk """ path = util.ensure_path(path) - getters = ["strings", "vectors"] if "strings" not in exclude: self.strings.from_disk(path / "strings.json") # TODO: add exclude? if "vectors" not in exclude: From 4f8daa4f003785e23cf2612683f78476dac7baca Mon Sep 17 00:00:00 2001 From: Jacobo Myerston <43222279+jmyerston@users.noreply.github.com> Date: Thu, 20 Jul 2023 02:16:01 -0700 Subject: [PATCH 14/74] Add Left and Right Pointing Angle Brackets as punctuation to ancient Greek (#12829) * Update universe.json * Update universe.json add some missing commas in the greCy's description. * Update punctuation.py Add mathematical left and right angle brackets as punctuation for ancient Greek for better tokenization. --- spacy/lang/grc/punctuation.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/spacy/lang/grc/punctuation.py b/spacy/lang/grc/punctuation.py index 8e9fc8bf2..59037617d 100644 --- a/spacy/lang/grc/punctuation.py +++ b/spacy/lang/grc/punctuation.py @@ -15,6 +15,7 @@ _prefixes = ( [ "†", "⸏", + "〈", ] + LIST_PUNCT + LIST_ELLIPSES @@ -31,6 +32,7 @@ _suffixes = ( + [ "†", "⸎", + "〉", r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])[\-\.⸏]", ] ) From 5888afa8840fc73afe09d45745c69de0b7828328 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Mon, 24 Jul 2023 10:32:56 +0200 Subject: [PATCH 15/74] Update numpy build constraints for numpy 1.25 (#12839) * Update numpy build constraints for numpy 1.25 Starting in numpy 1.25 (see https://github.com/numpy/numpy/releases/tag/v1.25.0), the numpy C API is backwards-compatible by default. For python 3.9+, we should be able to drop the specific numpy build requirements and use `numpy>=1.25`, which is currently backwards-compatible to `numpy>=1.19`. In the future, the python <3.9 requirements could be dropped and the lower numpy pin could correspond to the oldest supported version for the current lower python pin. * Turn off fail-fast * Revert "Turn off fail-fast" This reverts commit 4306f516bc4a6b3437b5393ff1b6b6ae54957d2d. * Update for python 3.6 * Fix typo --- build-constraints.txt | 5 +---- pyproject.toml | 3 ++- requirements.txt | 3 ++- setup.cfg | 8 +++++++- spacy/tests/package/test_requirements.py | 3 ++- 5 files changed, 14 insertions(+), 8 deletions(-) diff --git a/build-constraints.txt b/build-constraints.txt index c1e82f1b0..5540d634d 100644 --- a/build-constraints.txt +++ b/build-constraints.txt @@ -3,7 +3,4 @@ numpy==1.15.0; python_version<='3.7' and platform_machine!='aarch64' numpy==1.19.2; python_version<='3.7' and platform_machine=='aarch64' numpy==1.17.3; python_version=='3.8' and platform_machine!='aarch64' numpy==1.19.2; python_version=='3.8' and platform_machine=='aarch64' -numpy==1.19.3; python_version=='3.9' -numpy==1.21.3; python_version=='3.10' -numpy==1.23.2; python_version=='3.11' -numpy; python_version>='3.12' +numpy>=1.25.0; python_version>='3.9' diff --git a/pyproject.toml b/pyproject.toml index dcb5cf10d..c611c6c1c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,8 @@ requires = [ "preshed>=3.0.2,<3.1.0", "murmurhash>=0.28.0,<1.1.0", "thinc>=8.1.8,<8.2.0", - "numpy>=1.15.0", + "numpy>=1.15.0; python_version < '3.9'", + "numpy>=1.25.0; python_version >= '3.9'", ] build-backend = "setuptools.build_meta" diff --git a/requirements.txt b/requirements.txt index 2123ae976..9e787a223 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,7 +14,8 @@ pathy>=0.10.0 smart-open>=5.2.1,<7.0.0 weasel>=0.1.0,<0.2.0 # Third party dependencies -numpy>=1.15.0 +numpy>=1.15.0; python_version < "3.9" +numpy>=1.19.0; python_version >= "3.9" requests>=2.13.0,<3.0.0 tqdm>=4.38.0,<5.0.0 pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0 diff --git a/setup.cfg b/setup.cfg index 048bb3719..d94c9c73b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -32,8 +32,13 @@ project_urls = zip_safe = false include_package_data = true python_requires = >=3.6 +# NOTE: This section is superseded by pyproject.toml and will be removed in +# spaCy v4 setup_requires = cython>=0.25,<3.0 + # The newest supported pip for python 3.6 has bugs related to markers in + # this section, so this does not contain the same constraints as + # pyproject.toml numpy>=1.15.0 # We also need our Cython packages here to compile against cymem>=2.0.2,<2.1.0 @@ -57,7 +62,8 @@ install_requires = pathy>=0.10.0 smart-open>=5.2.1,<7.0.0 tqdm>=4.38.0,<5.0.0 - numpy>=1.15.0 + numpy>=1.15.0; python_version < "3.9" + numpy>=1.19.0; python_version >= "3.9" requests>=2.13.0,<3.0.0 pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0 jinja2 diff --git a/spacy/tests/package/test_requirements.py b/spacy/tests/package/test_requirements.py index fab1e8218..ff07c5b45 100644 --- a/spacy/tests/package/test_requirements.py +++ b/spacy/tests/package/test_requirements.py @@ -4,8 +4,8 @@ from pathlib import Path def test_build_dependencies(): # Check that library requirements are pinned exactly the same across different setup files. - # TODO: correct checks for numpy rather than ignoring libs_ignore_requirements = [ + "numpy", "pytest", "pytest-timeout", "mock", @@ -23,6 +23,7 @@ def test_build_dependencies(): ] # ignore language-specific packages that shouldn't be installed by all libs_ignore_setup = [ + "numpy", "fugashi", "natto-py", "pythainlp", From 1d216a7ea6d1cb56dc94f8394914c9be7c4fe0e8 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Mon, 24 Jul 2023 10:41:04 +0200 Subject: [PATCH 16/74] Update README for v3.6 (#12844) * Update most recent release * Switch from azure to GHA CI tests badge * Remove link to survey * Format --- README.md | 72 +++++++++++++++++++++++++++---------------------------- 1 file changed, 35 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index 59d3ee9ee..9a8f90749 100644 --- a/README.md +++ b/README.md @@ -6,23 +6,20 @@ spaCy is a library for **advanced Natural Language Processing** in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. -spaCy comes with -[pretrained pipelines](https://spacy.io/models) and -currently supports tokenization and training for **70+ languages**. It features -state-of-the-art speed and **neural network models** for tagging, -parsing, **named entity recognition**, **text classification** and more, -multi-task learning with pretrained **transformers** like BERT, as well as a +spaCy comes with [pretrained pipelines](https://spacy.io/models) and currently +supports tokenization and training for **70+ languages**. It features +state-of-the-art speed and **neural network models** for tagging, parsing, +**named entity recognition**, **text classification** and more, multi-task +learning with pretrained **transformers** like BERT, as well as a production-ready [**training system**](https://spacy.io/usage/training) and easy model packaging, deployment and workflow management. spaCy is commercial -open-source software, released under the [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE). +open-source software, released under the +[MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE). -💥 **We'd love to hear more about your experience with spaCy!** -[Fill out our survey here.](https://form.typeform.com/to/aMel9q9f) - -💫 **Version 3.5 out now!** +💫 **Version 3.6 out now!** [Check out the release notes here.](https://github.com/explosion/spaCy/releases) -[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8) +[![tests](https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/spaCy/actions/workflows/tests.yml) [![Current Release Version](https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square&logo=github)](https://github.com/explosion/spaCy/releases) [![pypi Version](https://img.shields.io/pypi/v/spacy.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy/) [![conda Version](https://img.shields.io/conda/vn/conda-forge/spacy.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/spacy) @@ -35,22 +32,22 @@ open-source software, released under the [MIT license](https://github.com/explos ## 📖 Documentation -| Documentation | | -| ----------------------------- | ---------------------------------------------------------------------- | -| ⭐️ **[spaCy 101]** | New to spaCy? Here's everything you need to know! | -| 📚 **[Usage Guides]** | How to use spaCy and its features. | -| 🚀 **[New in v3.0]** | New features, backwards incompatibilities and migration guide. | -| 🪐 **[Project Templates]** | End-to-end workflows you can clone, modify and run. | -| 🎛 **[API Reference]** | The detailed reference for spaCy's API. | -| 📦 **[Models]** | Download trained pipelines for spaCy. | -| 🌌 **[Universe]** | Plugins, extensions, demos and books from the spaCy ecosystem. | -| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. | -| 👩‍🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. | -| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. | -| 🛠 **[Changelog]** | Changes and version history. | -| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. | -| spaCy Tailored Pipelines | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more →](https://explosion.ai/spacy-tailored-pipelines)** | -| spaCy Tailored Pipelines | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more →](https://explosion.ai/spacy-tailored-analysis)** | +| Documentation | | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| ⭐️ **[spaCy 101]** | New to spaCy? Here's everything you need to know! | +| 📚 **[Usage Guides]** | How to use spaCy and its features. | +| 🚀 **[New in v3.0]** | New features, backwards incompatibilities and migration guide. | +| 🪐 **[Project Templates]** | End-to-end workflows you can clone, modify and run. | +| 🎛 **[API Reference]** | The detailed reference for spaCy's API. | +| 📦 **[Models]** | Download trained pipelines for spaCy. | +| 🌌 **[Universe]** | Plugins, extensions, demos and books from the spaCy ecosystem. | +| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. | +| 👩‍🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. | +| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. | +| 🛠 **[Changelog]** | Changes and version history. | +| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. | +| spaCy Tailored Pipelines | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more →](https://explosion.ai/spacy-tailored-pipelines)** | +| spaCy Tailored Pipelines | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more →](https://explosion.ai/spacy-tailored-analysis)** | [spacy 101]: https://spacy.io/usage/spacy-101 [new in v3.0]: https://spacy.io/usage/v3 @@ -58,7 +55,7 @@ open-source software, released under the [MIT license](https://github.com/explos [api reference]: https://spacy.io/api/ [models]: https://spacy.io/models [universe]: https://spacy.io/universe -[spaCy VS Code Extension]: https://github.com/explosion/spacy-vscode +[spacy vs code extension]: https://github.com/explosion/spacy-vscode [videos]: https://www.youtube.com/c/ExplosionAI [online course]: https://course.spacy.io [project templates]: https://github.com/explosion/projects @@ -92,7 +89,9 @@ more people can benefit from it. - State-of-the-art speed - Production-ready **training system** - Linguistically-motivated **tokenization** -- Components for named **entity recognition**, part-of-speech-tagging, dependency parsing, sentence segmentation, **text classification**, lemmatization, morphological analysis, entity linking and more +- Components for named **entity recognition**, part-of-speech-tagging, + dependency parsing, sentence segmentation, **text classification**, + lemmatization, morphological analysis, entity linking and more - Easily extensible with **custom components** and attributes - Support for custom models in **PyTorch**, **TensorFlow** and other frameworks - Built in **visualizers** for syntax and NER @@ -118,8 +117,8 @@ For detailed installation instructions, see the ### pip Using pip, spaCy releases are available as source packages and binary wheels. -Before you install spaCy and its dependencies, make sure that -your `pip`, `setuptools` and `wheel` are up to date. +Before you install spaCy and its dependencies, make sure that your `pip`, +`setuptools` and `wheel` are up to date. ```bash pip install -U pip setuptools wheel @@ -174,9 +173,9 @@ with the new version. ## 📦 Download model packages -Trained pipelines for spaCy can be installed as **Python packages**. This -means that they're a component of your application, just like any other module. -Models can be installed using spaCy's [`download`](https://spacy.io/api/cli#download) +Trained pipelines for spaCy can be installed as **Python packages**. This means +that they're a component of your application, just like any other module. Models +can be installed using spaCy's [`download`](https://spacy.io/api/cli#download) command, or manually by pointing pip to a path or URL. | Documentation | | @@ -242,8 +241,7 @@ do that depends on your system. | **Mac** | Install a recent version of [XCode](https://developer.apple.com/xcode/), including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled. | | **Windows** | Install a version of the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that matches the version that was used to compile your Python interpreter. | -For more details -and instructions, see the documentation on +For more details and instructions, see the documentation on [compiling spaCy from source](https://spacy.io/usage#source) and the [quickstart widget](https://spacy.io/usage#section-quickstart) to get the right commands for your platform and Python version. From e2b89012a2ecb7f778e72b57835232c08a218f22 Mon Sep 17 00:00:00 2001 From: Victoria <80417010+victorialslocum@users.noreply.github.com> Date: Mon, 24 Jul 2023 14:44:47 +0200 Subject: [PATCH 17/74] Add spacy-llm docs to website (#12782) * initial commit * update for v0.4.0 * Apply suggestions from code review * Fix formatting * Apply suggestions from code review * Update website/docs/api/large-language-models.mdx * Update website/docs/api/large-language-models.mdx * update usage page * Apply suggestions from review * Apply suggestions from review * fix links * fix relative links * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem * Apply suggestions from review * Add section on Llama 2. Format. --------- Co-authored-by: Raphael Mitsch Co-authored-by: Sofie Van Landeghem --- website/docs/api/large-language-models.mdx | 1488 ++++++++++++++++++ website/docs/usage/large-language-models.mdx | 512 ++++++ website/meta/sidebars.json | 14 +- website/pages/index.tsx | 102 +- 4 files changed, 2060 insertions(+), 56 deletions(-) create mode 100644 website/docs/api/large-language-models.mdx create mode 100644 website/docs/usage/large-language-models.mdx diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx new file mode 100644 index 000000000..cc8328790 --- /dev/null +++ b/website/docs/api/large-language-models.mdx @@ -0,0 +1,1488 @@ +--- +title: Large Language Models +teaser: Integrating LLMs into structured NLP pipelines +menu: + - ['Config', 'config'] + - ['Tasks', 'tasks'] + - ['Models', 'models'] + - ['Cache', 'cache'] + - ['Various Functions', 'various-functions'] +--- + +[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large +Language Models (LLMs) into spaCy, featuring a modular system for **fast +prototyping** and **prompting**, and turning unstructured responses into +**robust outputs** for various NLP tasks, **no training data** required. + +## Config {id="config"} + +`spacy-llm` exposes a `llm` factory that accepts the following configuration +options: + +| Argument | Description | +| ---------------- | ------------------------------------------------------------------------------------------------------- | +| `task` | An LLMTask can generate prompts and parse LLM responses. See [docs](#tasks). ~~Optional[LLMTask]~~ | +| `model` | Callable querying a specific LLM API. See [docs](#models). ~~Callable[[Iterable[Any]], Iterable[Any]]~~ | +| `cache` | Cache to use for caching prompts and responses per doc (batch). See [docs](#cache). ~~Cache~~ | +| `save_io` | Whether to save prompts/responses within `Doc.user_data["llm_io"]`. ~~bool~~ | +| `validate_types` | Whether to check if signatures of configured model and task are consistent. ~~bool~~ | + +An `llm` component is defined by two main settings: + +- A [**task**](#tasks), defining the prompt to send to the LLM as well as the + functionality to parse the resulting response back into structured fields on + the [Doc](/api/doc) objects. +- A [**model**](#models) defining the model and how to connect to it. Note that + `spacy-llm` supports both access to external APIs (such as OpenAI) as well as + access to self-hosted open-source LLMs (such as using Dolly through Hugging + Face). + +Moreover, `spacy-llm` exposes a customizable [**caching**](#cache) functionality +to avoid running the same document through an LLM service (be it local or +through a REST API) more than once. + +Finally, you can choose to save a stringified version of LLM prompts/responses +within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`. +`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM +component within the `nlp` pipeline. Each entry is itself a dictionary, with two +keys: `prompt` and `response`. + +A note on `validate_types`: by default, `spacy-llm` checks whether the +signatures of the `model` and `task` callables are consistent with each other +and emits a warning if they don't. `validate_types` can be set to `False` if you +want to disable this behavior. + +### Tasks {id="tasks"} + +A _task_ defines an NLP problem or question, that will be sent to the LLM via a +prompt. Further, the task defines how to parse the LLM's responses back into +structured information. All tasks are registered in the `llm_tasks` registry. + +#### task.generate_prompts {id="task-generate-prompts"} + +Takes a collection of documents, and returns a collection of "prompts", which +can be of type `Any`. Often, prompts are of type `str` - but this is not +enforced to allow for maximum flexibility in the framework. + +| Argument | Description | +| ----------- | ---------------------------------------- | +| `docs` | The input documents. ~~Iterable[Doc]~~ | +| **RETURNS** | The generated prompts. ~~Iterable[Any]~~ | + +#### task.parse_responses {id="task-parse-responses"} + +Takes a collection of LLM responses and the original documents, parses the +responses into structured information, and sets the annotations on the +documents. The `parse_responses` function is free to set the annotations in any +way, including `Doc` fields like `ents`, `spans` or `cats`, or using custom +defined fields. + +The `responses` are of type `Iterable[Any]`, though they will often be `str` +objects. This depends on the return type of the [model](#models). + +| Argument | Description | +| ----------- | ------------------------------------------ | +| `docs` | The input documents. ~~Iterable[Doc]~~ | +| `responses` | The generated prompts. ~~Iterable[Any]~~ | +| **RETURNS** | The annotated documents. ~~Iterable[Doc]~~ | + +#### spacy.Summarization.v1 {id="summarization-v1"} + +The `spacy.Summarization.v1` task supports both zero-shot and few-shot +prompting. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.Summarization.v1" +> examples = null +> max_n_words = null +> ``` + +| Argument | Description | +| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [summarization.jinja](./spacy_llm/tasks/templates/summarization.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `max_n_words` | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~ | +| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~ | + +The summarization task prompts the model for a concise summary of the provided +text. It optionally allows to limit the response to a certain number of tokens - +note that this requirement will be included in the prompt, but the task doesn't +perform a hard cut-off. It's hence possible that your summary exceeds +`max_n_words`. + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```yaml +- text: > + The United Nations, referred to informally as the UN, is an + intergovernmental organization whose stated purposes are to maintain + international peace and security, develop friendly relations among nations, + achieve international cooperation, and serve as a centre for harmonizing the + actions of nations. It is the world's largest international organization. + The UN is headquartered on international territory in New York City, and the + organization has other offices in Geneva, Nairobi, Vienna, and The Hague, + where the International Court of Justice is headquartered.\n\n The UN was + established after World War II with the aim of preventing future world wars, + and succeeded the League of Nations, which was characterized as + ineffective. + summary: + 'The UN is an international organization that promotes global peace, + cooperation, and harmony. Established after WWII, its purpose is to prevent + future world wars.' +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.Summarization.v1" +max_n_words = 20 +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "summarization_examples.yml" +``` + +#### spacy.NER.v2 {id="ner-v2"} + +The built-in NER task supports both zero-shot and few-shot prompting. This +version also supports explicitly defining the provided labels with custom +descriptions. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.NER.v2" +> labels = ["PERSON", "ORGANISATION", "LOCATION"] +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ | +| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +The NER task implementation doesn't currently ask the LLM for specific offsets, +but simply expects a list of strings that represent the enties in the document. +This means that a form of string matching is required. This can be configured by +the following parameters: + +- The `single_match` parameter is typically set to `False` to allow for multiple + matches. For instance, the response from the LLM might only mention the entity + "Paris" once, but you'd still want to mark it every time it occurs in the + document. +- The case-sensitive matching is typically set to `False` to be robust against + case variances in the LLM's output. +- The `alignment_mode` argument is used to match entities as returned by the LLM + to the tokens from the original `Doc` - specifically it's used as argument in + the call to [`doc.char_span()`](/api/doc#char_span). The `"strict"` mode will + only keep spans that strictly adhere to the given token boundaries. + `"contract"` will only keep those tokens that are fully within the given + range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will expand the + span to the next token boundaries, e.g. expanding `"New Y"` out to + `"New York"`. + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```yaml +- text: Jack and Jill went up the hill. + entities: + PERSON: + - Jack + - Jill + LOCATION: + - hill +- text: Jack fell down and broke his crown. + entities: + PERSON: + - Jack +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.NER.v2" +labels = PERSON,ORGANISATION,LOCATION +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "ner_examples.yml" +``` + +> Label descriptions can also be used with explicit examples to give as much +> info to the LLM model as possible. + +You can also write definitions for each label and provide them via the +`label_definitions` argument. This lets you tell the LLM exactly what you're +looking for rather than relying on the LLM to interpret its task given just the +label name. Label descriptions are freeform so you can write whatever you want +here, but through some experiments a brief description along with some examples +and counter examples seems to work quite well. + +```ini +[components.llm.task] +@llm_tasks = "spacy.NER.v2" +labels = PERSON,SPORTS_TEAM +[components.llm.task.label_definitions] +PERSON = "Extract any named individual in the text." +SPORTS_TEAM = "Extract the names of any professional sports team. e.g. Golden State Warriors, LA Lakers, Man City, Real Madrid" +``` + +#### spacy.NER.v1 {id="ner-v1"} + +The original version of the built-in NER task supports both zero-shot and +few-shot prompting. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.NER.v1" +> labels = PERSON,ORGANISATION,LOCATION +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | Comma-separated list of labels. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +The NER task implementation doesn't currently ask the LLM for specific offsets, +but simply expects a list of strings that represent the enties in the document. +This means that a form of string matching is required. This can be configured by +the following parameters: + +- The `single_match` parameter is typically set to `False` to allow for multiple + matches. For instance, the response from the LLM might only mention the entity + "Paris" once, but you'd still want to mark it every time it occurs in the + document. +- The case-sensitive matching is typically set to `False` to be robust against + case variances in the LLM's output. +- The `alignment_mode` argument is used to match entities as returned by the LLM + to the tokens from the original `Doc` - specifically it's used as argument in + the call to [`doc.char_span()`](/api/doc#char_span). The `"strict"` mode will + only keep spans that strictly adhere to the given token boundaries. + `"contract"` will only keep those tokens that are fully within the given + range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will expand the + span to the next token boundaries, e.g. expanding `"New Y"` out to + `"New York"`. + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```yaml +- text: Jack and Jill went up the hill. + entities: + PERSON: + - Jack + - Jill + LOCATION: + - hill +- text: Jack fell down and broke his crown. + entities: + PERSON: + - Jack +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.NER.v1" +labels = PERSON,ORGANISATION,LOCATION +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "ner_examples.yml" +``` + +#### spacy.SpanCat.v2 {id="spancat-v2"} + +The built-in SpanCat task is a simple adaptation of the NER task to support +overlapping entities and store its annotations in `doc.spans`. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.SpanCat.v2" +> labels = ["PERSON", "ORGANISATION", "LOCATION"] +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ | +| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +Except for the `spans_key` parameter, the SpanCat task reuses the configuration +from the NER task. Refer to [its documentation](#ner-v2) for more insight. + +#### spacy.SpanCat.v1 {id="spancat-v1"} + +The original version of the built-in SpanCat task is a simple adaptation of the +v1 NER task to support overlapping entities and store its annotations in +`doc.spans`. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.SpanCat.v1" +> labels = PERSON,ORGANISATION,LOCATION +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | Comma-separated list of labels. ~~str~~ | +| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | + +Except for the `spans_key` parameter, the SpanCat task reuses the configuration +from the NER task. Refer to [its documentation](#ner-v1) for more insight. + +#### spacy.TextCat.v3 {id="textcat-v3"} + +Version 3 (the most recent) of the built-in TextCat task supports both zero-shot +and few-shot prompting. It allows setting definitions of labels. Those +definitions are included in the prompt. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.TextCat.v3" +> labels = ["COMPLIMENT", "INSULT"] +> label_definitions = { +> "COMPLIMENT": "a polite expression of praise or admiration.", +> "INSULT": "a disrespectful or scornfully abusive remark or act." +> } +> examples = null +> ``` + +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `label_definitions` | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | +| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | +| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```json +[ + { + "text": "You look great!", + "answer": "Compliment" + }, + { + "text": "You are not very clever at all.", + "answer": "Insult" + } +] +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.TextCat.v3" +labels = ["COMPLIMENT", "INSULT"] +label_definitions = { + "COMPLIMENT": "a polite expression of praise or admiration.", + "INSULT": "a disrespectful or scornfully abusive remark or act." +} +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "textcat_examples.json" +``` + +#### spacy.TextCat.v2 {id="textcat-v2"} + +Version 2 of the built-in TextCat task supports both zero-shot and few-shot +prompting and includes an improved prompt template. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.TextCat.v2" +> labels = ["COMPLIMENT", "INSULT"] +> examples = null +> ``` + +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | +| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | +| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```json +[ + { + "text": "You look great!", + "answer": "Compliment" + }, + { + "text": "You are not very clever at all.", + "answer": "Insult" + } +] +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.TextCat.v2" +labels = ["COMPLIMENT", "INSULT"] +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "textcat_examples.json" +``` + +#### spacy.TextCat.v1 {id="textcat-v1"} + +Version 1 of the built-in TextCat task supports both zero-shot and few-shot +prompting. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.TextCat.v1" +> labels = COMPLIMENT,INSULT +> examples = null +> ``` + +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | Comma-separated list of labels. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Deafults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Deafults to `False`. ~~bool~~ | +| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Deafults to `True`. ~~bool~~ | +| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~ | + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```json +[ + { + "text": "You look great!", + "answer": "Compliment" + }, + { + "text": "You are not very clever at all.", + "answer": "Insult" + } +] +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.TextCat.v2" +labels = COMPLIMENT,INSULT +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "textcat_examples.json" +``` + +#### spacy.REL.v1 {id="rel-v1"} + +The built-in REL task supports both zero-shot and few-shot prompting. It relies +on an upstream NER component for entities extraction. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.REL.v1" +> labels = ["LivesIn", "Visits"] +> ``` + +| Argument | Description | +| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`rel.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.jinja). ~~str~~ | +| `label_description` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```json +{"text": "Laura bought a house in Boston with her husband Mark.", "ents": [{"start_char": 0, "end_char": 5, "label": "PERSON"}, {"start_char": 24, "end_char": 30, "label": "GPE"}, {"start_char": 48, "end_char": 52, "label": "PERSON"}], "relations": [{"dep": 0, "dest": 1, "relation": "LivesIn"}, {"dep": 2, "dest": 1, "relation": "LivesIn"}]} +{"text": "Michael travelled through South America by bike.", "ents": [{"start_char": 0, "end_char": 7, "label": "PERSON"}, {"start_char": 26, "end_char": 39, "label": "LOC"}], "relations": [{"dep": 0, "dest": 1, "relation": "Visits"}]} +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.REL.v1" +labels = ["LivesIn", "Visits"] +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "rel_examples.jsonl" +``` + +Note: the REL task relies on pre-extracted entities to make its prediction. +Hence, you'll need to add a component that populates `doc.ents` with recognized +spans to your spaCy pipeline and put it _before_ the REL component. + +#### spacy.Lemma.v1 {id="lemma-v1"} + +The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` +attribute in the doc's tokens accordingly. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.Lemma.v1" +> examples = null +> ``` + +| Argument | Description | +| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [lemma.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | + +`Lemma.v1` prompts the LLM to lemmatize the passed text and return the +lemmatized version as a list of tokens and their corresponding lemma. E. g. the +text `I'm buying ice cream for my friends` should invoke the response + +``` +I: I +'m: be +buying: buy +ice: ice +cream: cream +for: for +my: my +friends: friend +.: . +``` + +If for any given text/doc instance the number of lemmas returned by the LLM +doesn't match the number of tokens from the pipeline's tokenizer, no lemmas are +stored in the corresponding doc's tokens. Otherwise the tokens `.lemma_` +property is updated with the lemma suggested by the LLM. + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```yaml +- text: I'm buying ice cream. + lemmas: + - 'I': 'I' + - "'m": 'be' + - 'buying': 'buy' + - 'ice': 'ice' + - 'cream': 'cream' + - '.': '.' + +- text: I've watered the plants. + lemmas: + - 'I': 'I' + - "'ve": 'have' + - 'watered': 'water' + - 'the': 'the' + - 'plants': 'plant' + - '.': '.' +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.Lemma.v1" +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "lemma_examples.yml" +``` + +#### spacy.Sentiment.v1 {id="sentiment-v1"} + +Performs sentiment analysis on provided texts. Scores between 0 and 1 are stored +in `Doc._.sentiment` - the higher, the more positive. Note in cases of parsing +issues (e. g. in case of unexpected LLM responses) the value might be `None`. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.Sentiment.v1" +> examples = null +> ``` + +| Argument | Description | +| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [sentiment.jinja](./spacy_llm/tasks/templates/sentiment.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ | + +To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +```yaml +- text: 'This is horrifying.' + score: 0 +- text: 'This is underwhelming.' + score: 0.25 +- text: 'This is ok.' + score: 0.5 +- text: "I'm looking forward to this!" + score: 1.0 +``` + +```ini +[components.llm.task] +@llm_tasks = "spacy.Sentiment.v1" +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "sentiment_examples.yml" +``` + +#### spacy.NoOp.v1 {id="noop-v1"} + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.NoOp.v1" +> ``` + +This task is only useful for testing - it tells the LLM to do nothing, and does +not set any fields on the `docs`. + +### Models {id="models"} + +A _model_ defines which LLM model to query, and how to query it. It can be a +simple function taking a collection of prompts (consistent with the output type +of `task.generate_prompts()`) and returning a collection of responses +(consistent with the expected input of `parse_responses`). Generally speaking, +it's a function of type `Callable[[Iterable[Any]], Iterable[Any]]`, but specific +implementations can have other signatures, like +`Callable[[Iterable[str]], Iterable[str]]`. + +#### API Keys {id="api-keys"} + +Note that when using hosted services, you have to ensure that the proper API +keys are set as environment variables as described by the corresponding +provider's documentation. + +E. g. when using OpenAI, you have to get an API key from openai.com, and ensure +that the keys are set as environmental variables: + +```shell +export OPENAI_API_KEY="sk-..." +export OPENAI_API_ORG="org-..." +``` + +For Cohere it's + +```shell +export CO_API_KEY="..." +``` + +and for Anthropic + +```shell +export ANTHROPIC_API_KEY="..." +``` + +#### spacy.GPT-4.v1 {id="gpt-4"} + +OpenAI's `gpt-4` model family. + +> #### Example config: +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.GPT-4.v1" +> name = "gpt-4" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"gpt-4"`. ~~Literal["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.GPT-3-5.v1 {id="gpt-3-5"} + +OpenAI's `gpt-3-5` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.GPT-3-5.v1" +> name = "gpt-3.5-turbo" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"gpt-3.5-turbo"`. ~~Literal["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Text-Davinci.v1 {id="text-davinci"} + +OpenAI's `text-davinci` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Text-Davinci.v1" +> name = "text-davinci-003" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-davinci-003"`. ~~Literal["text-davinci-002", "text-davinci-003"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Code-Davinci.v1 {id="code-davinci"} + +OpenAI's `code-davinci` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Code-Davinci.v1" +> name = "code-davinci-002" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"code-davinci-002"`. ~~Literal["code-davinci-002"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Text-Curie.v1 {id="text-curie"} + +OpenAI's `text-curie` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Text-Curie.v1" +> name = "text-curie-001" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-curie-001"`. ~~Literal["text-curie-001"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Text-Babbage.v1 {id="text-babbage"} + +OpenAI's `text-babbage` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Text-Babbage.v1" +> name = "text-babbage-001" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-babbage-001"`. ~~Literal["text-babbage-001"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Text-Ada.v1 {id="text-ada"} + +OpenAI's `text-ada` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Text-Ada.v1" +> name = "text-ada-001" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-ada-001"`. ~~Literal["text-ada-001"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Davinci.v1 {id="davinci"} + +OpenAI's `davinci` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Davinci.v1 " +> name = "davinci" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"davinci"`. ~~Literal["davinci"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Curie.v1 {id="curie"} + +OpenAI's `curie` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Curie.v1 " +> name = "curie" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"curie"`. ~~Literal["curie"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Babbage.v1 {id="babbage"} + +OpenAI's `babbage` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Babbage.v1 " +> name = "babbage" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"babbage"`. ~~Literal["babbage"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Ada.v1 {id="ada"} + +OpenAI's `ada` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Ada.v1 " +> name = "ada" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"ada"`. ~~Literal["ada"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Command.v1 {id="command"} + +Cohere's `command` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Command.v1 " +> name = "command" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"command"`. ~~Literal["command", "command-light", "command-light-nightly", "command-nightly"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Claude-2.v1 {id="claude-2"} + +Anthropic's `claude-2` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Claude-2.v1 " +> name = "claude-2" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-2"`. ~~Literal["claude-2", "claude-2-100k"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Claude-1.v1 {id="claude-1"} + +Anthropic's `claude-1` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Claude-1.v1 " +> name = "claude-1" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1"`. ~~Literal["claude-1", "claude-1-100k"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Claude-instant-1.v1 {id="claude-instant-1"} + +Anthropic's `claude-instant-1` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Claude-instant-1.v1 " +> name = "claude-instant-1" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-instant-1"`. ~~Literal["claude-instant-1", "claude-instant-1-100k"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Claude-instant-1-1.v1 {id="claude-instant-1-1"} + +Anthropic's `claude-instant-1.1` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Claude-instant-1-1.v1 " +> name = "claude-instant-1.1" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-instant-1.1"`. ~~Literal["claude-instant-1.1", "claude-instant-1.1-100k"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Claude-1-0.v1 {id="claude-1-0"} + +Anthropic's `claude-1.0` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Claude-1-0.v1 " +> name = "claude-1.0" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.0"`. ~~Literal["claude-1.0"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Claude-1-2.v1 {id="claude-1-2"} + +Anthropic's `claude-1.2` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Claude-1-2.v1 " +> name = "claude-1.2" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.2"`. ~~Literal["claude-1.2"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Claude-1-3.v1 {id="claude-1-3"} + +Anthropic's `claude-1.3` model family. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Claude-1-3.v1 " +> name = "claude-1.3" +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.3"`. ~~Literal["claude-1.3", "claude-1.3-100k"]~~ | +| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | +| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | + +#### spacy.Dolly.v1 {id="dolly"} + +To use this model, ideally you have a GPU enabled and have installed +`transformers`, `torch` and CUDA in your virtual environment. This allows you to +have the setting `device=cuda:0` in your config, which ensures that the model is +loaded entirely on the GPU (and fails otherwise). + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Dolly.v1" +> name = "dolly-v2-3b" +> ``` + +| Argument | Description | +| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | The name of a Dolly model that is supported (e. g. "dolly-v2-3b" or "dolly-v2-12b"). ~~Literal["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | + +Supported models (see the +[Databricks models page](https://huggingface.co/databricks) on Hugging Face for +details): + +- `"databricks/dolly-v2-3b"` +- `"databricks/dolly-v2-7b"` +- `"databricks/dolly-v2-12b"` + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +#### spacy.Llama2.v1 {id="llama2"} + +To use this model, ideally you have a GPU enabled and have installed +`transformers`, `torch` and CUDA in your virtual environment. This allows you to +have the setting `device=cuda:0` in your config, which ensures that the model is +loaded entirely on the GPU (and fails otherwise). + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +Note that the chat models variants of Llama 2 are currently not supported. This +is because they need a particular prompting setup and don't add any discernible +benefits in the use case of `spacy-llm` (i. e. no interactive chat) compared the +completion model variants. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Llama2.v1" +> name = "llama2-7b-hf" +> ``` + +| Argument | Description | +| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `name` | The name of a Llama 2 model variant that is supported. Defaults to `"Llama-2-7b-hf"`. ~~Literal["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cache directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +#### spacy.Falcon.v1 {id="falcon"} + +To use this model, ideally you have a GPU enabled and have installed +`transformers`, `torch` and CUDA in your virtual environment. This allows you to +have the setting `device=cuda:0` in your config, which ensures that the model is +loaded entirely on the GPU (and fails otherwise). + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.Falcon.v1" +> name = "falcon-7b" +> ``` + +| Argument | Description | +| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `name` | The name of a Falcon model variant that is supported. Defaults to `"7b-instruct"`. ~~Literal["falcon-rw-1b", "falcon-7b", "falcon-7b-instruct", "falcon-40b-instruct"]~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cache directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +#### spacy.StableLM.v1 {id="stablelm"} + +To use this model, ideally you have a GPU enabled and have installed +`transformers`, `torch` and CUDA in your virtual environment. + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.StableLM.v1" +> name = "stablelm-tuned-alpha-7b" +> ``` + +| Argument | Description | +| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | The name of a StableLM model that is supported (e. g. "stablelm-tuned-alpha-7b"). ~~Literal["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | + +See the +[Stability AI StableLM GitHub repo](https://github.com/Stability-AI/StableLM/#stablelm-alpha) +for details. + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +#### spacy.OpenLLaMA.v1 {id="openllama"} + +To use this model, ideally you have a GPU enabled and have installed + +- `transformers[sentencepiece]` +- `torch` +- CUDA in your virtual environment. + +You can do so with + +```shell +python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" +``` + +If you don't have access to a GPU, you can install `accelerate` and +set`device_map=auto` instead, but be aware that this may result in some layers +getting distributed to the CPU or even the hard drive, which may ultimately +result in extremely slow queries. + +```shell +python -m pip install "accelerate>=0.16.0,<1.0" +``` + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.OpenLLaMA.v1" +> name = "open_llama_3b" +> ``` + +| Argument | Description | +| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | The name of a OpenLLaMA model that is supported. ~~Literal["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | + +See the +[OpenLM Research OpenLLaMA GitHub repo](https://github.com/openlm-research/open_llama) +for details. + +Note that Hugging Face will download this model the first time you use it - you +can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. + +#### LangChain models {id="langchain-models"} + +To use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval +part, make sure you have installed it first: + +```shell +python -m pip install "langchain==0.0.191" +# Or install with spacy-llm directly +python -m pip install "spacy-llm[extras]" +``` + +Note that LangChain currently only supports Python 3.9 and beyond. + +LangChain models in `spacy-llm` work slightly differently. `langchain`'s models +are parsed automatically, each LLM class in `langchain` has one entry in +`spacy-llm`'s registry. As `langchain`'s design has one class per API and not +per model, this results in registry entries like `langchain.OpenAI.v1` - i. e. +there is one registry entry per API and not per model (family), as for the REST- +and HuggingFace-based entries. + +The name of the model to be used has to be passed in via the `name` attribute. + +> #### Example config +> +> ```ini +> [components.llm.model] +> @llm_models = "langchain.OpenAI.v1" +> name = "gpt-3.5-turbo" +> query = {"@llm_queries": "spacy.CallLangChain.v1"} +> config = {"temperature": 0.3} +> ``` + +| Argument | Description | +| -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | The name of a mdodel supported by LangChain for this API. ~~str~~ | +| `config` | Configuration passed on to the LangChain model. Defaults to `{}`. ~~Dict[Any, Any]~~ | +| `query` | Function that executes the prompts. If `None`, defaults to `spacy.CallLangChain.v1`. ~~Optional[Callable[["langchain.llms.BaseLLM", Iterable[Any]], Iterable[Any]]]~~ | + +The default `query` (`spacy.CallLangChain.v1`) executes the prompts by running +`model(text)` for each given textual prompt. + +### Cache {id="cache"} + +Interacting with LLMs, either through an external API or a local instance, is +costly. Since developing an NLP pipeline generally means a lot of exploration +and prototyping, `spacy-llm` implements a built-in cache to avoid reprocessing +the same documents at each run that keeps batches of documents stored on disk. + +> #### Example config +> +> ```ini +> [components.llm.cache] +> @llm_misc = "spacy.BatchCache.v1" +> path = "path/to/cache" +> batch_size = 64 +> max_batches_in_mem = 4 +> ``` + +| Argument | Description | +| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | +| `path` | Cache directory. If `None`, no caching is performed, and this component will act as a NoOp. Defaults to `None`. ~~Optional[Union[str, Path]]~~ | +| `batch_size` | Number of docs in one batch (file). Once a batch is full, it will be peristed to disk. Defaults to 64. ~~int~~ | +| `max_batches_in_mem` | Max. number of batches to hold in memory. Allows you to limit the effect on your memory if you're handling a lot of docs. Defaults to 4. ~~int~~ | + +When retrieving a document, the `BatchCache` will first figure out what batch +the document belongs to. If the batch isn't in memory it will try to load the +batch from disk and then move it into memory. + +Note that since the cache is generated by a registered function, you can also +provide your own registered function returning your own cache implementation. If +you wish to do so, ensure that your cache object adheres to the `Protocol` +defined in `spacy_llm.ty.Cache`. + +### Various functions {id="various-functions"} + +#### spacy.FewShotReader.v1 {id="fewshotreader-v1"} + +This function is registered in spaCy's `misc` registry, and reads in examples +from a `.yml`, `.yaml`, `.json` or `.jsonl` file. It uses +[`srsly`](https://github.com/explosion/srsly) to read in these files and parses +them depending on the file extension. + +> #### Example config +> +> ```ini +> [components.llm.task.examples] +> @misc = "spacy.FewShotReader.v1" +> path = "ner_examples.yml" +> ``` + +| Argument | Description | +| -------- | ----------------------------------------------------------------------------------------------- | +| `path` | Path to an examples file with suffix `.yml`, `.yaml`, `.json` or `.jsonl`. ~~Union[str, Path]~~ | + +#### spacy.FileReader.v1 {id="filereader-v1"} + +This function is registered in spaCy's `misc` registry, and reads a file +provided to the `path` to return a `str` representation of its contents. This +function is typically used to read +[Jinja](https://jinja.palletsprojects.com/en/3.1.x/) files containing the prompt +template. + +> #### Example config +> +> ```ini +> [components.llm.task.template] +> @misc = "spacy.FileReader.v1" +> path = "ner_template.jinja2" +> ``` + +| Argument | Description | +| -------- | ------------------------------------------------- | +| `path` | Path to the file to be read. ~~Union[str, Path]~~ | + +#### Normalizer functions {id="normalizer-functions"} + +These functions provide simple normalizations for string comparisons, e.g. +between a list of specified labels and a label given in the raw text of the LLM +response. They are registered in spaCy's `misc` registry and have the signature +`Callable[[str], str]`. + +- `spacy.StripNormalizer.v1`: only apply `text.strip()` +- `spacy.LowercaseNormalizer.v1`: applies `text.strip().lower()` to compare + strings in a case-insensitive way. diff --git a/website/docs/usage/large-language-models.mdx b/website/docs/usage/large-language-models.mdx new file mode 100644 index 000000000..3c2c52c68 --- /dev/null +++ b/website/docs/usage/large-language-models.mdx @@ -0,0 +1,512 @@ +--- +title: Large Language Models +teaser: Integrating LLMs into structured NLP pipelines +menu: + - ['Motivation', 'motivation'] + - ['Install', 'install'] + - ['Usage', 'usage'] + - ['Logging', 'logging'] + - ['API', 'api'] + - ['Tasks', 'tasks'] + - ['Models', 'models'] +--- + +[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large +Language Models (LLMs) into spaCy pipelines, featuring a modular system for +**fast prototyping** and **prompting**, and turning unstructured responses into +**robust outputs** for various NLP tasks, **no training data** required. + +- Serializable `llm` **component** to integrate prompts into your pipeline +- **Modular functions** to define the [**task**](#tasks) (prompting and parsing) + and [**model**](#models) (model to use) +- Support for **hosted APIs** and self-hosted **open-source models** +- Integration with [`LangChain`](https://github.com/hwchase17/langchain) +- Access to + **[OpenAI API](https://platform.openai.com/docs/api-reference/introduction)**, + including GPT-4 and various GPT-3 models +- Built-in support for various **open-source** models hosted on + [Hugging Face](https://huggingface.co/) +- Usage examples for standard NLP tasks such as **Named Entity Recognition** and + **Text Classification** +- Easy implementation of **your own functions** via the + [registry](/api/top-level#registry) for custom prompting, parsing and model + integrations + +## Motivation {id="motivation"} + +Large Language Models (LLMs) feature powerful natural language understanding +capabilities. With only a few (and sometimes no) examples, an LLM can be +prompted to perform custom NLP tasks such as text categorization, named entity +recognition, coreference resolution, information extraction and more. + +Supervised learning is much worse than LLM prompting for prototyping, but for +many tasks it's much better for production. A transformer model that runs +comfortably on a single GPU is extremely powerful, and it's likely to be a +better choice for any task for which you have a well-defined output. You train +the model with anything from a few hundred to a few thousand labelled examples, +and it will learn to do exactly that. Efficiency, reliability and control are +all better with supervised learning, and accuracy will generally be higher than +LLM prompting as well. + +`spacy-llm` lets you have **the best of both worlds**. You can quickly +initialize a pipeline with components powered by LLM prompts, and freely mix in +components powered by other approaches. As your project progresses, you can look +at replacing some or all of the LLM-powered components as you require. + +Of course, there can be components in your system for which the power of an LLM +is fully justified. If you want a system that can synthesize information from +multiple documents in subtle ways and generate a nuanced summary for you, bigger +is better. However, even if your production system needs an LLM for some of the +task, that doesn't mean you need an LLM for all of it. Maybe you want to use a +cheap text classification model to help you find the texts to summarize, or +maybe you want to add a rule-based system to sanity check the output of the +summary. These before-and-after tasks are much easier with a mature and +well-thought-out library, which is exactly what spaCy provides. + +## Install {id="install"} + +`spacy-llm` will be installed automatically in future spaCy versions. For now, +you can run the following in the same virtual environment where you already have +`spacy` [installed](/usage). + +> ⚠️ This package is still experimental and it is possible that changes made to +> the interface will be breaking in minor version updates. + +```bash +python -m pip install spacy-llm +``` + +## Usage {id="usage"} + +The task and the model have to be supplied to the `llm` pipeline component using +the [config system](/api/data-formats#config). This package provides various +built-in functionality, as detailed in the [API](#-api) documentation. + +### Example 1: Add a text classifier using a GPT-3 model from OpenAI {id="example-1"} + +Create a new API key from openai.com or fetch an existing one, and ensure the +keys are set as environmental variables. For more background information, see +the [OpenAI](/api/large-language-models#gpt-3-5) section. + +Create a config file `config.cfg` containing at least the following (or see the +full example +[here](https://github.com/explosion/spacy-llm/tree/main/usage_examples/textcat_openai)): + +```ini +[nlp] +lang = "en" +pipeline = ["llm"] + +[components] + +[components.llm] +factory = "llm" + +[components.llm.task] +@llm_tasks = "spacy.TextCat.v2" +labels = ["COMPLIMENT", "INSULT"] + +[components.llm.model] +@llm_models = "spacy.GPT-3-5.v1" +config = {"temperature": 0.3} +``` + +Now run: + +```python +from spacy_llm.util import assemble + +nlp = assemble("config.cfg") +doc = nlp("You look gorgeous!") +print(doc.cats) +``` + +### Example 2: Add NER using an open-source model through Hugging Face {id="example-2"} + +To run this example, ensure that you have a GPU enabled, and `transformers`, +`torch` and CUDA installed. For more background information, see the +[DollyHF](/api/large-language-models#dolly) section. + +Create a config file `config.cfg` containing at least the following (or see the +full example +[here](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_dolly)): + +```ini +[nlp] +lang = "en" +pipeline = ["llm"] + +[components] + +[components.llm] +factory = "llm" + +[components.llm.task] +@llm_tasks = "spacy.NER.v2" +labels = ["PERSON", "ORGANISATION", "LOCATION"] + +[components.llm.model] +@llm_models = "spacy.Dolly.v1" +# For better performance, use dolly-v2-12b instead +name = "dolly-v2-3b" +``` + +Now run: + +```python +from spacy_llm.util import assemble + +nlp = assemble("config.cfg") +doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes") +print([(ent.text, ent.label_) for ent in doc.ents]) +``` + +Note that Hugging Face will download the `"databricks/dolly-v2-3b"` model the +first time you use it. You can +[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) +by setting the environmental variable `HF_HOME`. Also, you can upgrade the model +to be `"databricks/dolly-v2-12b"` for better performance. + +### Example 3: Create the component directly in Python {id="example-3"} + +The `llm` component behaves as any other component does, so adding it to an +existing pipeline follows the same pattern: + +```python +import spacy + +nlp = spacy.blank("en") +nlp.add_pipe( + "llm", + config={ + "task": { + "@llm_tasks": "spacy.NER.v2", + "labels": ["PERSON", "ORGANISATION", "LOCATION"] + }, + "model": { + "@llm_models": "spacy.gpt-3.5.v1", + }, + }, +) +nlp.initialize() +doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes") +print([(ent.text, ent.label_) for ent in doc.ents]) +``` + +Note that for efficient usage of resources, typically you would use +[`nlp.pipe(docs)`](/api/language#pipe) with a batch, instead of calling +`nlp(doc)` with a single document. + +### Example 4: Implement your own custom task {id="example-4"} + +To write a [`task`](#tasks), you need to implement two functions: +`generate_prompts` that takes a list of [`Doc`](/api/doc) objects and transforms +them into a list of prompts, and `parse_responses` that transforms the LLM +outputs into annotations on the [`Doc`](/api/doc), e.g. entity spans, text +categories and more. + +To register your custom task, decorate a factory function using the +`spacy_llm.registry.llm_tasks` decorator with a custom name that you can refer +to in your config. + +> 📖 For more details, see the +> [**usage example on writing your own task**](https://github.com/explosion/spacy-llm/tree/main/usage_examples#writing-your-own-task) + +```python +from typing import Iterable, List +from spacy.tokens import Doc +from spacy_llm.registry import registry +from spacy_llm.util import split_labels + + +@registry.llm_tasks("my_namespace.MyTask.v1") +def make_my_task(labels: str, my_other_config_val: float) -> "MyTask": + labels_list = split_labels(labels) + return MyTask(labels=labels_list, my_other_config_val=my_other_config_val) + + +class MyTask: + def __init__(self, labels: List[str], my_other_config_val: float): + ... + + def generate_prompts(self, docs: Iterable[Doc]) -> Iterable[str]: + ... + + def parse_responses( + self, docs: Iterable[Doc], responses: Iterable[str] + ) -> Iterable[Doc]: + ... +``` + +```ini +# config.cfg (excerpt) +[components.llm.task] +@llm_tasks = "my_namespace.MyTask.v1" +labels = LABEL1,LABEL2,LABEL3 +my_other_config_val = 0.3 +``` + +## Logging {id="logging"} + +spacy-llm has a built-in logger that can log the prompt sent to the LLM as well +as its raw response. This logger uses the debug level and by default has a +`logging.NullHandler()` configured. + +In order to use this logger, you can setup a simple handler like this: + +```python +import logging +import spacy_llm + + +spacy_llm.logger.addHandler(logging.StreamHandler()) +spacy_llm.logger.setLevel(logging.DEBUG) +``` + +> NOTE: Any `logging` handler will work here so you probably want to use some +> sort of rotating `FileHandler` as the generated prompts can be quite long, +> especially for tasks with few-shot examples. + +Then when using the pipeline you'll be able to view the prompt and response. + +E.g. with the config and code from [Example 1](#example-1) above: + +```python +from spacy_llm.util import assemble + + +nlp = assemble("config.cfg") +doc = nlp("You look gorgeous!") +print(doc.cats) +``` + +You will see `logging` output similar to: + +``` +Generated prompt for doc: You look gorgeous! + +You are an expert Text Classification system. Your task is to accept Text as input +and provide a category for the text based on the predefined labels. + +Classify the text below to any of the following labels: COMPLIMENT, INSULT +The task is non-exclusive, so you can provide more than one label as long as +they're comma-delimited. For example: Label1, Label2, Label3. +Do not put any other text in your answer, only one or more of the provided labels with nothing before or after. +If the text cannot be classified into any of the provided labels, answer `==NONE==`. + +Here is the text that needs classification + + +Text: +''' +You look gorgeous! +''' + +Model response for doc: You look gorgeous! +COMPLIMENT +``` + +`print(doc.cats)` to standard output should look like: + +``` +{'COMPLIMENT': 1.0, 'INSULT': 0.0} +``` + +## API {id="api"} + +`spacy-llm` exposes a `llm` factory with +[configurable settings](/api/large-language-models#config). + +An `llm` component is defined by two main settings: + +- A [**task**](#tasks), defining the prompt to send to the LLM as well as the + functionality to parse the resulting response back into structured fields on + the [Doc](/api/doc) objects. +- A [**model**](#models) defining the model to use and how to connect to it. + Note that `spacy-llm` supports both access to external APIs (such as OpenAI) + as well as access to self-hosted open-source LLMs (such as using Dolly through + Hugging Face). + +Moreover, `spacy-llm` exposes a customizable [**caching**](#cache) functionality +to avoid running the same document through an LLM service (be it local or +through a REST API) more than once. + +Finally, you can choose to save a stringified version of LLM prompts/responses +within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`. +`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM +component within the `nlp` pipeline. Each entry is itself a dictionary, with two +keys: `prompt` and `response`. + +A note on `validate_types`: by default, `spacy-llm` checks whether the +signatures of the `model` and `task` callables are consistent with each other +and emits a warning if they don't. `validate_types` can be set to `False` if you +want to disable this behavior. + +### Tasks {id="tasks"} + +A _task_ defines an NLP problem or question, that will be sent to the LLM via a +prompt. Further, the task defines how to parse the LLM's responses back into +structured information. All tasks are registered in the `llm_tasks` registry. + +Practically speaking, a task should adhere to the `Protocol` `LLMTask` defined +in [`ty.py`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/ty.py). +It needs to define a `generate_prompts` function and a `parse_responses` +function. + +| Task | Description | +| --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [`task.generate_prompts`](/api/large-language-models#task-generate-prompts) | Takes a collection of documents, and returns a collection of "prompts", which can be of type `Any`. | +| [`task.parse_responses`](/api/large-language-models#task-parse-responses) | Takes a collection of LLM responses and the original documents, parses the responses into structured information, and sets the annotations on the documents. | + +Moreover, the task may define an optional [`scorer` method](/api/scorer#score). +It should accept an iterable of `Example`s as input and return a score +dictionary. If the `scorer` method is defined, `spacy-llm` will call it to +evaluate the component. + +| Component | Description | +| ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. | +| [`spacy.NER.v2`](/api/large-language-models#ner-v2) | The built-in NER task supports both zero-shot and few-shot prompting. This version also supports explicitly defining the provided labels with custom descriptions. | +| [`spacy.NER.v1`](/api/large-language-models#ner-v1) | The original version of the built-in NER task supports both zero-shot and few-shot prompting. | +| [`spacy.SpanCat.v2`](/api/large-language-models#spancat-v2) | The built-in SpanCat task is a simple adaptation of the NER task to support overlapping entities and store its annotations in `doc.spans`. | +| [`spacy.SpanCat.v1`](/api/large-language-models#spancat-v1) | The original version of the built-in SpanCat task is a simple adaptation of the v1 NER task to support overlapping entities and store its annotations in `doc.spans`. | +| [`spacy.TextCat.v3`](/api/large-language-models#textcat-v3) | Version 3 (the most recent) of the built-in TextCat task supports both zero-shot and few-shot prompting. It allows setting definitions of labels. | +| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 of the built-in TextCat task supports both zero-shot and few-shot prompting and includes an improved prompt template. | +| [`spacy.TextCat.v1`](/api/large-language-models#textcat-v1) | Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting. | +| [`spacy.REL.v1`](/api/large-language-models#rel-v1) | The built-in REL task supports both zero-shot and few-shot prompting. It relies on an upstream NER component for entities extraction. | +| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` attribute in the doc's tokens accordingly. | +| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. | +| [`spacy.NoOp.v1`](/api/large-language-models#noop-v1) | This task is only useful for testing - it tells the LLM to do nothing, and does not set any fields on the `docs`. | + +#### Providing examples for few-shot prompts {id="few-shot-prompts"} + +All built-in tasks support few-shot prompts, i. e. including examples in a +prompt. Examples can be supplied in two ways: (1) as a separate file containing +only examples or (2) by initializing `llm` with a `get_examples()` callback +(like any other pipeline component). + +##### (1) Few-shot example file + +A file containing examples for few-shot prompting can be configured like this: + +```ini +[components.llm.task] +@llm_tasks = "spacy.NER.v2" +labels = PERSON,ORGANISATION,LOCATION +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "ner_examples.yml" +``` + +The supplied file has to conform to the format expected by the required task +(see the task documentation further down). + +##### (2) Initializing the `llm` component with a `get_examples()` callback + +Alternatively, you can initialize your `nlp` pipeline by providing a +`get_examples` callback for [`nlp.initialize`](/api/language#initialize) and +setting `n_prompt_examples` to a positive number to automatically fetch a few +examples for few-shot learning. Set `n_prompt_examples` to `-1` to use all +examples as part of the few-shot learning prompt. + +```ini +[initialize.components.llm] +n_prompt_examples = 3 +``` + +### Model {id="models"} + +A _model_ defines which LLM model to query, and how to query it. It can be a +simple function taking a collection of prompts (consistent with the output type +of `task.generate_prompts()`) and returning a collection of responses +(consistent with the expected input of `parse_responses`). Generally speaking, +it's a function of type `Callable[[Iterable[Any]], Iterable[Any]]`, but specific +implementations can have other signatures, like +`Callable[[Iterable[str]], Iterable[str]]`. + +All built-in models are registered in `llm_models`. If no model is specified, +the repo currently connects to the `OpenAI` API by default using REST, and +accesses the `"gpt-3.5-turbo"` model. + +Currently three different approaches to use LLMs are supported: + +1. `spacy-llm`s native REST interface. This is the default for all hosted models + (e. g. OpenAI, Cohere, Anthropic, ...). +2. A HuggingFace integration that allows to run a limited set of HF models + locally. +3. A LangChain integration that allows to run any model supported by LangChain + (hosted or locally). + +Approaches 1. and 2 are the default for hosted model and local models, +respectively. Alternatively you can use LangChain to access hosted or local +models by specifying one of the models registered with the `langchain.` prefix. + + +_Why LangChain if there are also are a native REST and a HuggingFace interface? When should I use what?_ + +Third-party libraries like `langchain` focus on prompt management, integration +of many different LLM APIs, and other related features such as conversational +memory or agents. `spacy-llm` on the other hand emphasizes features we consider +useful in the context of NLP pipelines utilizing LLMs to process documents +(mostly) independent from each other. It makes sense that the feature sets of +such third-party libraries and `spacy-llm` aren't identical - and users might +want to take advantage of features not available in `spacy-llm`. + +The advantage of implementing our own REST and HuggingFace integrations is that +we can ensure a larger degree of stability and robustness, as we can guarantee +backwards-compatibility and more smoothly integrated error handling. + +If however there are features or APIs not natively covered by `spacy-llm`, it's +trivial to utilize LangChain to cover this - and easy to customize the prompting +mechanism, if so required. + + + + +Note that when using hosted services, you have to ensure that the [proper API +keys](/api/large-language-models#api-keys) are set as environment variables as described by the corresponding +provider's documentation. + + + +| Component | Description | +| ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------ | +| [`spacy.GPT-4.v1`](/api/large-language-models#gpt-4) | OpenAI’s `gpt-4` model family. | +| [`spacy.GPT-3-5.v1`](/api/large-language-models#gpt-3-5) | OpenAI’s `gpt-3-5` model family. | +| [`spacy.Text-Davinci.v1`](/api/large-language-models#text-davinci) | OpenAI’s `text-davinci` model family. | +| [`spacy.Code-Davinci.v1`](/api/large-language-models#code-davinci) | OpenAI’s `code-davinci` model family. | +| [`spacy.Text-Curie.v1`](/api/large-language-models#text-curie) | OpenAI’s `text-curie` model family. | +| [`spacy.Text-Babbage.v1`](/api/large-language-models#text-babbage) | OpenAI’s `text-babbage` model family. | +| [`spacy.Text-Ada.v1`](/api/large-language-models#text-ada) | OpenAI’s `text-ada` model family. | +| [`spacy.Davinci.v1`](/api/large-language-models#davinci) | OpenAI’s `davinci` model family. | +| [`spacy.Curie.v1`](/api/large-language-models#curie) | OpenAI’s `curie` model family. | +| [`spacy.Babbage.v1`](/api/large-language-models#babbage) | OpenAI’s `babbage` model family. | +| [`spacy.Ada.v1`](/api/large-language-models#ada) | OpenAI’s `ada` model family. | +| [`spacy.Command.v1`](/api/large-language-models#command) | Cohere’s `command` model family. | +| [`spacy.Claude-1.v1`](/api/large-language-models#claude-1) | Anthropic’s `claude-1` model family. | +| [`spacy.Claude-instant-1.v1`](/api/large-language-models#claude-instant-1) | Anthropic’s `claude-instant-1` model family. | +| [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#claude-instant-1-1) | Anthropic’s `claude-instant-1.1` model family. | +| [`spacy.Claude-1-0.v1`](/api/large-language-models#claude-1-0) | Anthropic’s `claude-1.0` model family. | +| [`spacy.Claude-1-2.v1`](/api/large-language-models#claude-1-2) | Anthropic’s `claude-1.2` model family. | +| [`spacy.Claude-1-3.v1`](/api/large-language-models#claude-1-3) | Anthropic’s `claude-1.3` model family. | +| [`spacy.Dolly.v1`](/api/large-language-models#dolly) | Dolly models through [Databricks](https://huggingface.co/databricks) on HuggingFace. | +| [`spacy.Falcon.v1`](/api/large-language-models#falcon) | Falcon model through HuggingFace. | +| [`spacy.StableLM.v1`](/api/large-language-models#stablelm) | StableLM model through HuggingFace. | +| [`spacy.OpenLLaMA.v1`](/api/large-language-models#openllama) | OpenLLaMA model through HuggingFace. | +| [LangChain models](/api/large-language-models#langchain-models) | LangChain models for API retrieval. | + +### Cache {id="cache"} + +Interacting with LLMs, either through an external API or a local instance, is +costly. Since developing an NLP pipeline generally means a lot of exploration +and prototyping, `spacy-llm` implements a built-in +[cache](/api/large-language-models#cache) to avoid reprocessing the same +documents at each run that keeps batches of documents stored on disk. + +### Various functions {id="various-functions"} + +| Component | Description | +| ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [`spacy.FewShotReader.v1`](/api/large-language-models#fewshotreader-v1) | This function is registered in spaCy's `misc` registry, and reads in examples from a `.yml`, `.yaml`, `.json` or `.jsonl` file. It uses [`srsly`](https://github.com/explosion/srsly) to read in these files and parses them depending on the file extension. | +| [`spacy.FileReader.v1`](/api/large-language-models#filereader-v1) | This function is registered in spaCy's `misc` registry, and reads a file provided to the `path` to return a `str` representation of its contents. This function is typically used to read [Jinja](https://jinja.palletsprojects.com/en/3.1.x/) files containing the prompt template. | +| [Normalizer functions](/api/large-language-models#normalizer-functions) | These functions provide simple normalizations for string comparisons, e.g. between a list of specified labels and a label given in the raw text of the LLM response. | diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json index 04102095f..033f71b12 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -26,16 +26,19 @@ { "text": "Processing Pipelines", "url": "/usage/processing-pipelines" }, { "text": "Embeddings & Transformers", - "url": "/usage/embeddings-transformers", + "url": "/usage/embeddings-transformers" + }, + { + "text": "Large Language Models", + "url": "/usage/large-language-models", "tag": "new" }, - { "text": "Training Models", "url": "/usage/training", "tag": "new" }, + { "text": "Training Models", "url": "/usage/training" }, { "text": "Layers & Model Architectures", - "url": "/usage/layers-architectures", - "tag": "new" + "url": "/usage/layers-architectures" }, - { "text": "spaCy Projects", "url": "/usage/projects", "tag": "new" }, + { "text": "spaCy Projects", "url": "/usage/projects" }, { "text": "Saving & Loading", "url": "/usage/saving-loading" }, { "text": "Visualizers", "url": "/usage/visualizers" } ] @@ -102,6 +105,7 @@ { "text": "EntityLinker", "url": "/api/entitylinker" }, { "text": "EntityRecognizer", "url": "/api/entityrecognizer" }, { "text": "EntityRuler", "url": "/api/entityruler" }, + { "text": "Large Language Models", "url": "/api/large-language-models" }, { "text": "Lemmatizer", "url": "/api/lemmatizer" }, { "text": "Morphologizer", "url": "/api/morphologizer" }, { "text": "SentenceRecognizer", "url": "/api/sentencerecognizer" }, diff --git a/website/pages/index.tsx b/website/pages/index.tsx index fc0dba378..089d75b52 100644 --- a/website/pages/index.tsx +++ b/website/pages/index.tsx @@ -106,50 +106,21 @@ const Landing = () => {

- - - + + The spacy-llm package + {' '} + integrates Large Language Models (LLMs) into spaCy, featuring a modular + system for fast prototyping and prompting, + and turning unstructured responses into robust outputs for + various NLP tasks, no training data required.

-

- - Get a custom spaCy pipeline, tailor-made for your NLP problem by - spaCy's core developers. - -

-
    -
  • - Streamlined. Nobody knows spaCy better than we do. Send - us your pipeline requirements and we'll be ready to start producing - your solution in no time at all. -
    • -
  • - Production ready. spaCy pipelines are robust and easy - to deploy. You'll get a complete spaCy project folder which is - ready to spacy project run. -
    • -
  • - Predictable. You'll know exactly what you're - going to get and what it's going to cost. We quote fees up-front, - let you try before you buy, and don't charge for over-runs at our - end — all the risk is on us. -
    • -
  • - Maintainable. spaCy is an industry standard, and - we'll deliver your pipeline with full code, data, tests and - documentation, so your team can retrain, update and extend the solution - as your requirements change. -
    • -
{

- spaCy v3.0 features all new transformer-based pipelines{' '} - that bring spaCy's accuracy right up to the current{' '} - state-of-the-art. You can use any pretrained transformer to - train your own pipelines, and even share one transformer between multiple - components with multi-task learning. Training is now fully - configurable and extensible, and you can define your own custom models using{' '} - PyTorch, TensorFlow and other frameworks. + + +

+

+ + Get a custom spaCy pipeline, tailor-made for your NLP problem by + spaCy's core developers. + +

+
    +
  • + Streamlined. Nobody knows spaCy better than we do. Send + us your pipeline requirements and we'll be ready to start producing + your solution in no time at all. +
    • +
  • + Production ready. spaCy pipelines are robust and easy + to deploy. You'll get a complete spaCy project folder which is + ready to spacy project run. +
    • +
  • + Predictable. You'll know exactly what you're + going to get and what it's going to cost. We quote fees up-front, + let you try before you buy, and don't charge for over-runs at our + end — all the risk is on us. +
    • +
  • + Maintainable. spaCy is an industry standard, and + we'll deliver your pipeline with full code, data, tests and + documentation, so your team can retrain, update and extend the solution + as your requirements change. +
    • +
{ small >

- + Date: Mon, 24 Jul 2023 16:58:27 +0200 Subject: [PATCH 18/74] Switch from distutils to setuptools/sysconfig (#12853) Additionally remove outdated `is_new_osx` check and settings. --- setup.py | 31 +++---------------------------- 1 file changed, 3 insertions(+), 28 deletions(-) diff --git a/setup.py b/setup.py index 243554c7a..3b6fae37b 100755 --- a/setup.py +++ b/setup.py @@ -1,10 +1,9 @@ #!/usr/bin/env python from setuptools import Extension, setup, find_packages import sys -import platform import numpy -from distutils.command.build_ext import build_ext -from distutils.sysconfig import get_python_inc +from setuptools.command.build_ext import build_ext +from sysconfig import get_path from pathlib import Path import shutil from Cython.Build import cythonize @@ -88,30 +87,6 @@ COPY_FILES = { } -def is_new_osx(): - """Check whether we're on OSX >= 10.7""" - if sys.platform != "darwin": - return False - mac_ver = platform.mac_ver()[0] - if mac_ver.startswith("10"): - minor_version = int(mac_ver.split(".")[1]) - if minor_version >= 7: - return True - else: - return False - return False - - -if is_new_osx(): - # On Mac, use libc++ because Apple deprecated use of - # libstdc - COMPILE_OPTIONS["other"].append("-stdlib=libc++") - LINK_OPTIONS["other"].append("-lc++") - # g++ (used by unix compiler on mac) links to libstdc++ as a default lib. - # See: https://stackoverflow.com/questions/1653047/avoid-linking-to-libstdc - LINK_OPTIONS["other"].append("-nodefaultlibs") - - # By subclassing build_extensions we have the actual compiler that will be used which is really known only after finalize_options # http://stackoverflow.com/questions/724664/python-distutils-how-to-get-a-compiler-that-is-going-to-be-used class build_ext_options: @@ -204,7 +179,7 @@ def setup_package(): include_dirs = [ numpy.get_include(), - get_python_inc(plat_specific=True), + get_path("include"), ] ext_modules = [] ext_modules.append( From 98799d849e2a78f54797178e45fbe37c5161943d Mon Sep 17 00:00:00 2001 From: Madeesh Kannan Date: Wed, 26 Jul 2023 13:56:31 +0200 Subject: [PATCH 19/74] `SpanCat`: Remove invalid `threshold` config argument (#12860) --- website/docs/api/spancategorizer.mdx | 1 - 1 file changed, 1 deletion(-) diff --git a/website/docs/api/spancategorizer.mdx b/website/docs/api/spancategorizer.mdx index 81a473ac2..2b63d31ce 100644 --- a/website/docs/api/spancategorizer.mdx +++ b/website/docs/api/spancategorizer.mdx @@ -67,7 +67,6 @@ architectures and their arguments and hyperparameters. > ```python > from spacy.pipeline.spancat import DEFAULT_SPANCAT_SINGLELABEL_MODEL > config = { -> "threshold": 0.5, > "spans_key": "labeled_spans", > "model": DEFAULT_SPANCAT_SINGLELABEL_MODEL, > "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]}, From 51b9655470aca59df3adacc4b05c77cde6e5579b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Wed, 26 Jul 2023 16:05:53 +0200 Subject: [PATCH 20/74] Added OdyCy to spaCy Universe (#12826) * Added OdyCy to spaCy Universe * Replaced template tags Co-authored-by: Adriane Boyd --------- Co-authored-by: Adriane Boyd --- website/meta/universe.json | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/website/meta/universe.json b/website/meta/universe.json index 75ec5fb5c..041ebbff8 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -67,6 +67,33 @@ "category": ["pipeline", "research"], "tags": ["latin"] }, + { + "id": "odycy", + "title": "OdyCy", + "slogan": "General-purpose language pipelines for premodern Greek.", + "description": "Academically validated modular NLP pipelines for premodern Greek. odyCy achieves state of the art performance on multiple tasks on unseen test data from the Universal Dependencies Perseus treebank, and performs second best on the PROIEL treebank’s test set on even more tasks. In addition performance also seems relatively stable across the two evaluation datasets in comparison with other NLP pipelines. OdyCy is being used at the Center for Humanities Computing for preprocessing and analyzing Ancient Greek corpora for New Testament research, meaning that you can expect consistent maintenance and improvements.", + "github": "centre-for-humanities-computing/odyCy", + "code_example": [ + "# To install the high-accuracy transformer-based pipeline", + "# pip install https://huggingface.co/chcaa/grc_odycy_joint_trf/resolve/main/grc_odycy_joint_trf-any-py3-none-any.whl", + "import spacy", + "", + "nlp = spacy.load('grc_odycy_joint_trf')", + "", + "doc = nlp('τὴν γοῦν Ἀττικὴν ἐκ τοῦ ἐπὶ πλεῖστον διὰ τὸ λεπτόγεων ἀστασίαστον οὖσαν ἄνθρωποι ᾤκουν οἱ αὐτοὶ αἰεί.')" + ], + "code_language": "python", + "url": "https://centre-for-humanities-computing.github.io/odyCy/", + "thumb": "https://raw.githubusercontent.com/centre-for-humanities-computing/odyCy/7b94fec60679d06272dca88a4dcfe0f329779aea/docs/_static/logo.svg", + "image": "https://github.com/centre-for-humanities-computing/odyCy/raw/main/docs/_static/logo_with_text_below.svg", + "author": "Jan Kostkan, Márton Kardos (Center for Humanities Computing, Aarhus University)", + "author_links": { + "github": "centre-for-humanities-computing", + "website": "https://chc.au.dk/" + }, + "category": ["pipeline", "standalone", "research"], + "tags": ["ancient Greek"] + }, { "id": "spacy-wasm", "title": "spacy-wasm", From 9ffa5d8a1582bfb9720585792fe5294c36d55370 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 28 Jul 2023 15:48:36 +0200 Subject: [PATCH 21/74] Remove ray extra (#12870) --- setup.cfg | 2 -- 1 file changed, 2 deletions(-) diff --git a/setup.cfg b/setup.cfg index d94c9c73b..116e40f2c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -82,8 +82,6 @@ lookups = spacy_lookups_data>=1.0.3,<1.1.0 transformers = spacy_transformers>=1.1.2,<1.3.0 -ray = - spacy_ray>=0.1.0,<1.0.0 cuda = cupy>=5.0.0b4,<13.0.0 cuda80 = From 49055ed7c825bc5c6ce828ddf0ff0bcbf1527a7b Mon Sep 17 00:00:00 2001 From: Victoria <80417010+victorialslocum@users.noreply.github.com> Date: Mon, 31 Jul 2023 09:39:00 +0200 Subject: [PATCH 22/74] Add cli for finding locations of registered func (#12757) * Add cli for finding locations of registered func * fixes: naming and typing * isort * update naming * remove to find-function * remove file:// bit * use registry name if given and exit gracefully if a registry was not found * clean up failure msg * specify registry_name options * mypy fixes * return location for internal usage * add documentation * more mypy fixes * clean up example * add section to menu * add tests --------- Co-authored-by: svlandeg --- spacy/cli/__init__.py | 1 + spacy/cli/find_function.py | 69 +++++++++++++++++++++++++++++++++++++ spacy/tests/test_cli_app.py | 34 ++++++++++++++++++ website/docs/api/cli.mdx | 36 +++++++++++++++---- 4 files changed, 133 insertions(+), 7 deletions(-) create mode 100644 spacy/cli/find_function.py diff --git a/spacy/cli/__init__.py b/spacy/cli/__init__.py index 549a27616..60fe718c7 100644 --- a/spacy/cli/__init__.py +++ b/spacy/cli/__init__.py @@ -14,6 +14,7 @@ from .debug_diff import debug_diff # noqa: F401 from .debug_model import debug_model # noqa: F401 from .download import download # noqa: F401 from .evaluate import evaluate # noqa: F401 +from .find_function import find_function # noqa: F401 from .find_threshold import find_threshold # noqa: F401 from .info import info # noqa: F401 from .init_config import fill_config, init_config # noqa: F401 diff --git a/spacy/cli/find_function.py b/spacy/cli/find_function.py new file mode 100644 index 000000000..f99ce2adc --- /dev/null +++ b/spacy/cli/find_function.py @@ -0,0 +1,69 @@ +from typing import Optional, Tuple + +from catalogue import RegistryError +from wasabi import msg + +from ..util import registry +from ._util import Arg, Opt, app + + +@app.command("find-function") +def find_function_cli( + # fmt: off + func_name: str = Arg(..., help="Name of the registered function."), + registry_name: Optional[str] = Opt(None, "--registry", "-r", help="Name of the catalogue registry."), + # fmt: on +): + """ + Find the module, path and line number to the file the registered + function is defined in, if available. + + func_name (str): Name of the registered function. + registry_name (Optional[str]): Name of the catalogue registry. + + DOCS: https://spacy.io/api/cli#find-function + """ + if not registry_name: + registry_names = registry.get_registry_names() + for name in registry_names: + if registry.has(name, func_name): + registry_name = name + break + + if not registry_name: + msg.fail( + f"Couldn't find registered function: '{func_name}'", + exits=1, + ) + + assert registry_name is not None + find_function(func_name, registry_name) + + +def find_function(func_name: str, registry_name: str) -> Tuple[str, int]: + registry_desc = None + try: + registry_desc = registry.find(registry_name, func_name) + except RegistryError as e: + msg.fail( + f"Couldn't find registered function: '{func_name}' in registry '{registry_name}'", + ) + msg.fail(f"{e}", exits=1) + assert registry_desc is not None + + registry_path = None + line_no = None + if registry_desc["file"]: + registry_path = registry_desc["file"] + line_no = registry_desc["line_no"] + + if not registry_path or not line_no: + msg.fail( + f"Couldn't find path to registered function: '{func_name}' in registry '{registry_name}'", + exits=1, + ) + assert registry_path is not None + assert line_no is not None + + msg.good(f"Found registered function '{func_name}' at {registry_path}:{line_no}") + return str(registry_path), int(line_no) diff --git a/spacy/tests/test_cli_app.py b/spacy/tests/test_cli_app.py index 3a426113b..0e6d8e252 100644 --- a/spacy/tests/test_cli_app.py +++ b/spacy/tests/test_cli_app.py @@ -233,3 +233,37 @@ def test_project_push_pull(project_dir): result = CliRunner().invoke(app, ["project", "pull", remote, str(project_dir)]) assert result.exit_code == 0 assert test_file.is_file() + + +def test_find_function_valid(): + # example of architecture in main code base + function = "spacy.TextCatBOW.v2" + result = CliRunner().invoke(app, ["find-function", function, "-r", "architectures"]) + assert f"Found registered function '{function}'" in result.stdout + assert "textcat.py" in result.stdout + + result = CliRunner().invoke(app, ["find-function", function]) + assert f"Found registered function '{function}'" in result.stdout + assert "textcat.py" in result.stdout + + # example of architecture in spacy-legacy + function = "spacy.TextCatBOW.v1" + result = CliRunner().invoke(app, ["find-function", function]) + assert f"Found registered function '{function}'" in result.stdout + assert "spacy_legacy" in result.stdout + assert "textcat.py" in result.stdout + + +def test_find_function_invalid(): + # invalid registry + function = "spacy.TextCatBOW.v2" + registry = "foobar" + result = CliRunner().invoke( + app, ["find-function", function, "--registry", registry] + ) + assert f"Unknown function registry: '{registry}'" in result.stdout + + # invalid function + function = "spacy.TextCatBOW.v666" + result = CliRunner().invoke(app, ["find-function", function]) + assert f"Couldn't find registered function: '{function}'" in result.stdout diff --git a/website/docs/api/cli.mdx b/website/docs/api/cli.mdx index 6a87f78b8..d63ac6e1d 100644 --- a/website/docs/api/cli.mdx +++ b/website/docs/api/cli.mdx @@ -7,6 +7,7 @@ menu: - ['info', 'info'] - ['validate', 'validate'] - ['init', 'init'] + - ['find-function', 'find-function'] - ['convert', 'convert'] - ['debug', 'debug'] - ['train', 'train'] @@ -251,6 +252,27 @@ $ python -m spacy init labels [config_path] [output_path] [--code] [--verbose] [ | overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ | | **CREATES** | The label files. | +## find-function {id="find-function",version="3.7",tag="command"} + +Find the module, path and line number to the file for a given registered +function. This functionality is helpful to understand where registered +functions, as used in the config file, are defined. + +```bash +$ python -m spacy find-function [func_name] [--registry] +``` + +> #### Example +> +> ```bash +> $ python -m spacy find-function spacy.TextCatBOW.v1 +> ``` + +| Name | Description | +| ------------------ | ----------------------------------------------------- | +| `func_name` | Name of the registered function. ~~str (positional)~~ | +| `--registry`, `-r` | Name of the catalogue registry. ~~str (option)~~ | + ## convert {id="convert",tag="command"} Convert files into spaCy's @@ -1651,10 +1673,10 @@ $ python -m spacy huggingface-hub push [whl_path] [--org] [--msg] [--verbose] > $ python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl > ``` -| Name | Description | -| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | -| `whl_path` | The path to the `.whl` file packaged with [`spacy package`](https://spacy.io/api/cli#package). ~~Path(positional)~~ | -| `--org`, `-o` | Optional name of organization to which the pipeline should be uploaded. ~~str (option)~~ | -| `--msg`, `-m` | Commit message to use for update. Defaults to `"Update spaCy pipeline"`. ~~str (option)~~ | -| `--verbose`, `-V` | Output additional info for debugging, e.g. the full generated hub metadata. ~~bool (flag)~~ | -| **UPLOADS** | The pipeline to the hub. | +| Name | Description | +| ----------------- | ------------------------------------------------------------------------------------------------------------------- | +| `whl_path` | The path to the `.whl` file packaged with [`spacy package`](https://spacy.io/api/cli#package). ~~Path(positional)~~ | +| `--org`, `-o` | Optional name of organization to which the pipeline should be uploaded. ~~str (option)~~ | +| `--msg`, `-m` | Commit message to use for update. Defaults to `"Update spaCy pipeline"`. ~~str (option)~~ | +| `--verbose`, `-V` | Output additional info for debugging, e.g. the full generated hub metadata. ~~bool (flag)~~ | +| **UPLOADS** | The pipeline to the hub. | From c9e9dccf7951bd474c5be8ca46dad7290ae86ee2 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Mon, 31 Jul 2023 10:47:57 +0200 Subject: [PATCH 23/74] Add displaCy data structures to docs (2) (#12875) * Add data structures to docs * Adjusted descriptions for more consistency * Add _optional_ flag to parameters * Add tests and adjust optional title key in doc * Add title to dep visualizations * fix typo --------- Co-authored-by: thomashacker --- spacy/displacy/render.py | 2 + spacy/tests/test_displacy.py | 72 +++++++++++++++++ website/docs/api/top-level.mdx | 124 +++++++++++++++++++++++++++++ website/docs/usage/visualizers.mdx | 3 +- 4 files changed, 200 insertions(+), 1 deletion(-) diff --git a/spacy/displacy/render.py b/spacy/displacy/render.py index 47407bcb7..758dc07d5 100644 --- a/spacy/displacy/render.py +++ b/spacy/displacy/render.py @@ -313,6 +313,8 @@ class DependencyRenderer: self.lang = settings.get("lang", DEFAULT_LANG) render_id = f"{id_prefix}-{i}" svg = self.render_svg(render_id, p["words"], p["arcs"]) + if p.get("title"): + svg = TPL_TITLE.format(title=p.get("title")) + svg rendered.append(svg) if page: content = "".join([TPL_FIGURE.format(content=svg) for svg in rendered]) diff --git a/spacy/tests/test_displacy.py b/spacy/tests/test_displacy.py index 1570f8d09..e9b5a9aba 100644 --- a/spacy/tests/test_displacy.py +++ b/spacy/tests/test_displacy.py @@ -350,6 +350,78 @@ def test_displacy_render_wrapper(en_vocab): displacy.set_render_wrapper(lambda html: html) +def test_displacy_render_manual_dep(): + """Test displacy.render with manual data for dep style""" + parsed_dep = { + "words": [ + {"text": "This", "tag": "DT"}, + {"text": "is", "tag": "VBZ"}, + {"text": "a", "tag": "DT"}, + {"text": "sentence", "tag": "NN"}, + ], + "arcs": [ + {"start": 0, "end": 1, "label": "nsubj", "dir": "left"}, + {"start": 2, "end": 3, "label": "det", "dir": "left"}, + {"start": 1, "end": 3, "label": "attr", "dir": "right"}, + ], + "title": "Title", + } + html = displacy.render([parsed_dep], style="dep", manual=True) + for word in parsed_dep["words"]: + assert word["text"] in html + assert word["tag"] in html + + +def test_displacy_render_manual_ent(): + """Test displacy.render with manual data for ent style""" + parsed_ents = [ + { + "text": "But Google is starting from behind.", + "ents": [{"start": 4, "end": 10, "label": "ORG"}], + }, + { + "text": "But Google is starting from behind.", + "ents": [{"start": -100, "end": 100, "label": "COMPANY"}], + "title": "Title", + }, + ] + + html = displacy.render(parsed_ents, style="ent", manual=True) + for parsed_ent in parsed_ents: + assert parsed_ent["ents"][0]["label"] in html + if "title" in parsed_ent: + assert parsed_ent["title"] in html + + +def test_displacy_render_manual_span(): + """Test displacy.render with manual data for span style""" + parsed_spans = [ + { + "text": "Welcome to the Bank of China.", + "spans": [ + {"start_token": 3, "end_token": 6, "label": "ORG"}, + {"start_token": 5, "end_token": 6, "label": "GPE"}, + ], + "tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."], + }, + { + "text": "Welcome to the Bank of China.", + "spans": [ + {"start_token": 3, "end_token": 6, "label": "ORG"}, + {"start_token": 5, "end_token": 6, "label": "GPE"}, + ], + "tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."], + "title": "Title", + }, + ] + + html = displacy.render(parsed_spans, style="span", manual=True) + for parsed_span in parsed_spans: + assert parsed_span["spans"][0]["label"] in html + if "title" in parsed_span: + assert parsed_span["title"] in html + + def test_displacy_options_case(): ents = ["foo", "BAR"] colors = {"FOO": "red", "bar": "green"} diff --git a/website/docs/api/top-level.mdx b/website/docs/api/top-level.mdx index 64ec342cd..37e86a4bc 100644 --- a/website/docs/api/top-level.mdx +++ b/website/docs/api/top-level.mdx @@ -343,6 +343,130 @@ use with the `manual=True` argument in `displacy.render`. | `options` | Span-specific visualisation options. ~~Dict[str, Any]~~ | | **RETURNS** | Generated entities keyed by text (original text) and ents. ~~dict~~ | +### Visualizer data structures {id="displacy_structures"} + +You can use displaCy's data format to manually render data. This can be useful +if you want to visualize output from other libraries. You can find examples of +displaCy's different data formats below. + +> #### DEP example data structure +> +> ```json +> { +> "words": [ +> { "text": "This", "tag": "DT" }, +> { "text": "is", "tag": "VBZ" }, +> { "text": "a", "tag": "DT" }, +> { "text": "sentence", "tag": "NN" } +> ], +> "arcs": [ +> { "start": 0, "end": 1, "label": "nsubj", "dir": "left" }, +> { "start": 2, "end": 3, "label": "det", "dir": "left" }, +> { "start": 1, "end": 3, "label": "attr", "dir": "right" } +> ] +> } +> ``` + +#### Dependency Visualizer data structure {id="structure-dep"} + +| Dictionary Key | Description | +| -------------- | ----------------------------------------------------------------------------------------------------------- | +| `words` | List of dictionaries describing a word token (see structure below). ~~List[Dict[str, Any]]~~ | +| `arcs` | List of dictionaries describing the relations between words (see structure below). ~~List[Dict[str, Any]]~~ | +| _Optional_ | | +| `title` | Title of the visualization. ~~Optional[str]~~ | +| `settings` | Dependency Visualizer options (see [here](/api/top-level#displacy_options)). ~~Dict[str, Any]~~ | + + + +| Dictionary Key | Description | +| -------------- | ---------------------------------------- | +| `text` | Text content of the word. ~~str~~ | +| `tag` | Fine-grained part-of-speech. ~~str~~ | +| `lemma` | Base form of the word. ~~Optional[str]~~ | + + + + + +| Dictionary Key | Description | +| -------------- | ---------------------------------------------------- | +| `start` | The index of the starting token. ~~int~~ | +| `end` | The index of the ending token. ~~int~~ | +| `label` | The type of dependency relation. ~~str~~ | +| `dir` | Direction of the relation (`left`, `right`). ~~str~~ | + + + +> #### ENT example data structure +> +> ```json +> { +> "text": "But Google is starting from behind.", +> "ents": [{ "start": 4, "end": 10, "label": "ORG" }] +> } +> ``` + +#### Named Entity Recognition data structure {id="structure-ent"} + +| Dictionary Key | Description | +| -------------- | ------------------------------------------------------------------------------------------- | +| `text` | String representation of the document text. ~~str~~ | +| `ents` | List of dictionaries describing entities (see structure below). ~~List[Dict[str, Any]]~~ | +| _Optional_ | | +| `title` | Title of the visualization. ~~Optional[str]~~ | +| `settings` | Entity Visualizer options (see [here](/api/top-level#displacy_options)). ~~Dict[str, Any]~~ | + + + +| Dictionary Key | Description | +| -------------- | ---------------------------------------------------------------------- | +| `start` | The index of the first character of the entity. ~~int~~ | +| `end` | The index of the last character of the entity. (not inclusive) ~~int~~ | +| `label` | Label attached to the entity. ~~str~~ | +| _Optional_ | | +| `kb_id` | `KnowledgeBase` ID. ~~str~~ | +| `kb_url` | `KnowledgeBase` URL. ~~str~~ | + + + +> #### SPAN example data structure +> +> ```json +> { +> "text": "Welcome to the Bank of China.", +> "spans": [ +> { "start_token": 3, "end_token": 6, "label": "ORG" }, +> { "start_token": 5, "end_token": 6, "label": "GPE" } +> ], +> "tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."] +> } +> ``` + +#### Span Classification data structure {id="structure-span"} + +| Dictionary Key | Description | +| -------------- | ----------------------------------------------------------------------------------------- | +| `text` | String representation of the document text. ~~str~~ | +| `spans` | List of dictionaries describing spans (see structure below). ~~List[Dict[str, Any]]~~ | +| `tokens` | List of word tokens. ~~List[str]~~ | +| _Optional_ | | +| `title` | Title of the visualization. ~~Optional[str]~~ | +| `settings` | Span Visualizer options (see [here](/api/top-level#displacy_options)). ~~Dict[str, Any]~~ | + + + +| Dictionary Key | Description | +| -------------- | ------------------------------------------------------------- | +| `start_token` | The index of the first token of the span in `tokens`. ~~int~~ | +| `end_token` | The index of the last token of the span in `tokens`. ~~int~~ | +| `label` | Label attached to the span. ~~str~~ | +| _Optional_ | | +| `kb_id` | `KnowledgeBase` ID. ~~str~~ | +| `kb_url` | `KnowledgeBase` URL. ~~str~~ | + + + ### Visualizer options {id="displacy_options"} The `options` argument lets you specify additional settings for each visualizer. diff --git a/website/docs/usage/visualizers.mdx b/website/docs/usage/visualizers.mdx index 1ac931753..e73c4a16a 100644 --- a/website/docs/usage/visualizers.mdx +++ b/website/docs/usage/visualizers.mdx @@ -349,7 +349,8 @@ or [SyntaxNet](https://github.com/tensorflow/models/tree/master/research/syntaxnet). If you set `manual=True` on either `render()` or `serve()`, you can pass in data in displaCy's format as a dictionary (instead of `Doc` objects). There are -helper functions for converting `Doc` objects to displaCy's format for use with +helper functions for converting `Doc` objects to +[displaCy's format](/api/top-level#displacy_structures) for use with `manual=True`: [`displacy.parse_deps`](/api/top-level#displacy.parse_deps), [`displacy.parse_ents`](/api/top-level#displacy.parse_ents), and [`displacy.parse_spans`](/api/top-level#displacy.parse_spans). From 186889ec9c4c7a5b6b9b88bea0c6c74a763998ee Mon Sep 17 00:00:00 2001 From: Andy Friedman Date: Mon, 31 Jul 2023 04:52:32 -0400 Subject: [PATCH 24/74] added entry for SaysWho (#12828) * Update universe.json added entry for Sayswho * Update universe.json updated sayswho entry * Update universe.json * Update website/meta/universe.json * Update website/meta/universe.json --------- Co-authored-by: Adriane Boyd --- website/meta/universe.json | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/website/meta/universe.json b/website/meta/universe.json index 041ebbff8..2ed8b4b41 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -17,6 +17,31 @@ "category": ["extension"], "tags": [] }, + { + "id": "sayswho", + "title": "SaysWho", + "slogan": "Quote identification, attribution and resolution", + "description": "A Python package for identifying and attributing quotes in text. It uses a combination of spaCy functionality, logic and grammar to find quotes and their speakers, then uses the spaCy coreferencing model to better clarify who is speaking. Currently English only.", + "github": "afriedman412/sayswho", + "pip": "sayswho", + "code_language": "python", + "author": "Andy Friedman", + "author_links": { + "twitter": "@steadynappin", + "github": "afriedman412" + }, + "code_example": [ + "from sayswho import SaysWho", + "text = open(\"path/to/your/text_file.txt\").read()", + "sw = SaysWho()", + "sw.attribute(text)", + + "sw.expand_match() # see quote/cluster matches", + "sw.render_to_html() # output your text, quotes and cluster matches to an html file called \"temp.html\"" + ], + "category": ["standalone"], + "tags": ["attribution", "coref", "text-processing"] + }, { "id": "parsigs", "title": "parsigs", From a0a195688f87ffa6c37d7d4994a37a2acfd7e2ad Mon Sep 17 00:00:00 2001 From: Peter Baumgartner <5107405+pmbaumgartner@users.noreply.github.com> Date: Mon, 31 Jul 2023 08:45:04 -0400 Subject: [PATCH 25/74] Tests for CLI app - `init config` generates `train`-able config (#12173) * remove migration support form * initial test commit * add fixture * add combo test * pull out parameter example data * fix formatting on examples * remove unused import * remove unncessary fmt:off instructions * only set logger level if verbose flag is explicitly set --------- Co-authored-by: svlandeg --- spacy/cli/assemble.py | 3 +- spacy/cli/find_threshold.py | 4 +- spacy/cli/init_pipeline.py | 9 +- spacy/cli/train.py | 3 +- spacy/tests/test_cli_app.py | 161 +++++++++++++++++++++++++++++++++++- 5 files changed, 172 insertions(+), 8 deletions(-) diff --git a/spacy/cli/assemble.py b/spacy/cli/assemble.py index ee2500b27..f74bbacb5 100644 --- a/spacy/cli/assemble.py +++ b/spacy/cli/assemble.py @@ -40,7 +40,8 @@ def assemble_cli( DOCS: https://spacy.io/api/cli#assemble """ - util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) + if verbose: + util.logger.setLevel(logging.DEBUG) # Make sure all files and paths exists if they are needed if not config_path or (str(config_path) != "-" and not config_path.exists()): msg.fail("Config file not found", config_path, exits=1) diff --git a/spacy/cli/find_threshold.py b/spacy/cli/find_threshold.py index 7aa32c0c6..48077fa51 100644 --- a/spacy/cli/find_threshold.py +++ b/spacy/cli/find_threshold.py @@ -52,8 +52,8 @@ def find_threshold_cli( DOCS: https://spacy.io/api/cli#find-threshold """ - - util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) + if verbose: + util.logger.setLevel(logging.DEBUG) import_code(code_path) find_threshold( model=model, diff --git a/spacy/cli/init_pipeline.py b/spacy/cli/init_pipeline.py index 13202cb60..21eea8edf 100644 --- a/spacy/cli/init_pipeline.py +++ b/spacy/cli/init_pipeline.py @@ -39,7 +39,8 @@ def init_vectors_cli( you can use in the [initialize] block of your config to initialize a model with vectors. """ - util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) + if verbose: + util.logger.setLevel(logging.DEBUG) msg.info(f"Creating blank nlp object for language '{lang}'") nlp = util.get_lang_class(lang)() if jsonl_loc is not None: @@ -87,7 +88,8 @@ def init_pipeline_cli( use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU") # fmt: on ): - util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) + if verbose: + util.logger.setLevel(logging.DEBUG) overrides = parse_config_overrides(ctx.args) import_code(code_path) setup_gpu(use_gpu) @@ -116,7 +118,8 @@ def init_labels_cli( """Generate JSON files for the labels in the data. This helps speed up the training process, since spaCy won't have to preprocess the data to extract the labels.""" - util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) + if verbose: + util.logger.setLevel(logging.DEBUG) if not output_path.exists(): output_path.mkdir(parents=True) overrides = parse_config_overrides(ctx.args) diff --git a/spacy/cli/train.py b/spacy/cli/train.py index 8bdabd39c..c72e13b26 100644 --- a/spacy/cli/train.py +++ b/spacy/cli/train.py @@ -47,7 +47,8 @@ def train_cli( DOCS: https://spacy.io/api/cli#train """ - util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) + if verbose: + util.logger.setLevel(logging.DEBUG) overrides = parse_config_overrides(ctx.args) import_code(code_path) train(config_path, output_path, use_gpu=use_gpu, overrides=overrides) diff --git a/spacy/tests/test_cli_app.py b/spacy/tests/test_cli_app.py index 0e6d8e252..e6f3b5912 100644 --- a/spacy/tests/test_cli_app.py +++ b/spacy/tests/test_cli_app.py @@ -6,7 +6,7 @@ import srsly from typer.testing import CliRunner from spacy.cli._util import app, get_git_version -from spacy.tokens import Doc, DocBin +from spacy.tokens import Doc, DocBin, Span from .util import make_tempdir, normalize_whitespace @@ -267,3 +267,162 @@ def test_find_function_invalid(): function = "spacy.TextCatBOW.v666" result = CliRunner().invoke(app, ["find-function", function]) assert f"Couldn't find registered function: '{function}'" in result.stdout + + +example_words_1 = ["I", "like", "cats"] +example_words_2 = ["I", "like", "dogs"] +example_lemmas_1 = ["I", "like", "cat"] +example_lemmas_2 = ["I", "like", "dog"] +example_tags = ["PRP", "VBP", "NNS"] +example_morphs = [ + "Case=Nom|Number=Sing|Person=1|PronType=Prs", + "Tense=Pres|VerbForm=Fin", + "Number=Plur", +] +example_deps = ["nsubj", "ROOT", "dobj"] +example_pos = ["PRON", "VERB", "NOUN"] +example_ents = ["O", "O", "I-ANIMAL"] +example_spans = [(2, 3, "ANIMAL")] + +TRAIN_EXAMPLE_1 = dict( + words=example_words_1, + lemmas=example_lemmas_1, + tags=example_tags, + morphs=example_morphs, + deps=example_deps, + heads=[1, 1, 1], + pos=example_pos, + ents=example_ents, + spans=example_spans, + cats={"CAT": 1.0, "DOG": 0.0}, +) +TRAIN_EXAMPLE_2 = dict( + words=example_words_2, + lemmas=example_lemmas_2, + tags=example_tags, + morphs=example_morphs, + deps=example_deps, + heads=[1, 1, 1], + pos=example_pos, + ents=example_ents, + spans=example_spans, + cats={"CAT": 0.0, "DOG": 1.0}, +) + + +@pytest.mark.slow +@pytest.mark.parametrize( + "component,examples", + [ + ("tagger", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]), + ("morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]), + ("trainable_lemmatizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]), + ("parser", [TRAIN_EXAMPLE_1] * 30), + ("ner", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]), + ("spancat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]), + ("textcat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]), + ], +) +def test_init_config_trainable(component, examples, en_vocab): + if component == "textcat": + train_docs = [] + for example in examples: + doc = Doc(en_vocab, words=example["words"]) + doc.cats = example["cats"] + train_docs.append(doc) + elif component == "spancat": + train_docs = [] + for example in examples: + doc = Doc(en_vocab, words=example["words"]) + doc.spans["sc"] = [ + Span(doc, start, end, label) for start, end, label in example["spans"] + ] + train_docs.append(doc) + else: + train_docs = [] + for example in examples: + # cats, spans are not valid kwargs for instantiating a Doc + example = {k: v for k, v in example.items() if k not in ("cats", "spans")} + doc = Doc(en_vocab, **example) + train_docs.append(doc) + + with make_tempdir() as d_in: + train_bin = DocBin(docs=train_docs) + train_bin.to_disk(d_in / "train.spacy") + dev_bin = DocBin(docs=train_docs) + dev_bin.to_disk(d_in / "dev.spacy") + init_config_result = CliRunner().invoke( + app, + [ + "init", + "config", + f"{d_in}/config.cfg", + "--lang", + "en", + "--pipeline", + component, + ], + ) + assert init_config_result.exit_code == 0 + train_result = CliRunner().invoke( + app, + [ + "train", + f"{d_in}/config.cfg", + "--paths.train", + f"{d_in}/train.spacy", + "--paths.dev", + f"{d_in}/dev.spacy", + "--output", + f"{d_in}/model", + ], + ) + assert train_result.exit_code == 0 + assert Path(d_in / "model" / "model-last").exists() + + +@pytest.mark.slow +@pytest.mark.parametrize( + "component,examples", + [("tagger,parser,morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2] * 15)], +) +def test_init_config_trainable_multiple(component, examples, en_vocab): + train_docs = [] + for example in examples: + example = {k: v for k, v in example.items() if k not in ("cats", "spans")} + doc = Doc(en_vocab, **example) + train_docs.append(doc) + + with make_tempdir() as d_in: + train_bin = DocBin(docs=train_docs) + train_bin.to_disk(d_in / "train.spacy") + dev_bin = DocBin(docs=train_docs) + dev_bin.to_disk(d_in / "dev.spacy") + init_config_result = CliRunner().invoke( + app, + [ + "init", + "config", + f"{d_in}/config.cfg", + "--lang", + "en", + "--pipeline", + component, + ], + ) + assert init_config_result.exit_code == 0 + train_result = CliRunner().invoke( + app, + [ + "train", + f"{d_in}/config.cfg", + "--paths.train", + f"{d_in}/train.spacy", + "--paths.dev", + f"{d_in}/dev.spacy", + "--output", + f"{d_in}/model", + ], + ) + assert train_result.exit_code == 0 + assert Path(d_in / "model" / "model-last").exists() From 0fe43f40f1390092dc265c9f2b2cef58ae06cc58 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 1 Aug 2023 15:46:08 +0200 Subject: [PATCH 26/74] Support registered vectors (#12492) * Support registered vectors * Format * Auto-fill [nlp] on load from config and from bytes/disk * Only auto-fill [nlp] * Undo all changes to Language.from_disk * Expand BaseVectors These methods are needed in various places for training and vector similarity. * isort * More linting * Only fill [nlp.vectors] * Update spacy/vocab.pyx Co-authored-by: Sofie Van Landeghem * Revert changes to test related to auto-filling [nlp] * Add vectors registry * Rephrase error about vocab methods for vectors * Switch to dummy implementation for BaseVectors.to_ops * Add initial draft of docs * Remove example from BaseVectors docs * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem * Update website/docs/api/basevectors.mdx Co-authored-by: Sofie Van Landeghem * Fix type and lint bpemb example * Update website/docs/api/basevectors.mdx --------- Co-authored-by: Sofie Van Landeghem --- spacy/default_config.cfg | 3 + spacy/errors.py | 2 + spacy/language.py | 18 +- spacy/ml/staticvectors.py | 11 +- spacy/schemas.py | 1 + spacy/util.py | 1 + spacy/vectors.pyx | 75 ++++++++- spacy/vocab.pyx | 18 +- website/docs/api/basevectors.mdx | 143 ++++++++++++++++ website/docs/api/vectors.mdx | 9 +- .../docs/usage/embeddings-transformers.mdx | 159 ++++++++++++++++++ website/meta/sidebars.json | 1 + 12 files changed, 425 insertions(+), 16 deletions(-) create mode 100644 website/docs/api/basevectors.mdx diff --git a/spacy/default_config.cfg b/spacy/default_config.cfg index 694fb732f..b005eef40 100644 --- a/spacy/default_config.cfg +++ b/spacy/default_config.cfg @@ -26,6 +26,9 @@ batch_size = 1000 [nlp.tokenizer] @tokenizers = "spacy.Tokenizer.v1" +[nlp.vectors] +@vectors = "spacy.Vectors.v1" + # The pipeline components and their models [components] diff --git a/spacy/errors.py b/spacy/errors.py index 225cb9c86..14ec669a3 100644 --- a/spacy/errors.py +++ b/spacy/errors.py @@ -553,6 +553,8 @@ class Errors(metaclass=ErrorsWithCodes): "during training, make sure to include it in 'annotating components'") # New errors added in v3.x + E849 = ("The vocab only supports {method} for vectors of type " + "spacy.vectors.Vectors, not {vectors_type}.") E850 = ("The PretrainVectors objective currently only supports default or " "floret vectors, not {mode} vectors.") E851 = ("The 'textcat' component labels should only have values of 0 or 1, " diff --git a/spacy/language.py b/spacy/language.py index b144b2c32..26152b90a 100644 --- a/spacy/language.py +++ b/spacy/language.py @@ -65,6 +65,7 @@ from .util import ( registry, warn_if_jupyter_cupy, ) +from .vectors import BaseVectors from .vocab import Vocab, create_vocab PipeCallable = Callable[[Doc], Doc] @@ -158,6 +159,7 @@ class Language: max_length: int = 10**6, meta: Dict[str, Any] = {}, create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None, + create_vectors: Optional[Callable[["Vocab"], BaseVectors]] = None, batch_size: int = 1000, **kwargs, ) -> None: @@ -198,6 +200,10 @@ class Language: if vocab is True: vectors_name = meta.get("vectors", {}).get("name") vocab = create_vocab(self.lang, self.Defaults, vectors_name=vectors_name) + if not create_vectors: + vectors_cfg = {"vectors": self._config["nlp"]["vectors"]} + create_vectors = registry.resolve(vectors_cfg)["vectors"] + vocab.vectors = create_vectors(vocab) else: if (self.lang and vocab.lang) and (self.lang != vocab.lang): raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang)) @@ -1765,6 +1771,10 @@ class Language: ).merge(config) if "nlp" not in config: raise ValueError(Errors.E985.format(config=config)) + # fill in [nlp.vectors] if not present (as a narrower alternative to + # auto-filling [nlp] from the default config) + if "vectors" not in config["nlp"]: + config["nlp"]["vectors"] = {"@vectors": "spacy.Vectors.v1"} config_lang = config["nlp"].get("lang") if config_lang is not None and config_lang != cls.lang: raise ValueError( @@ -1796,6 +1806,7 @@ class Language: filled["nlp"], validate=validate, schema=ConfigSchemaNlp ) create_tokenizer = resolved_nlp["tokenizer"] + create_vectors = resolved_nlp["vectors"] before_creation = resolved_nlp["before_creation"] after_creation = resolved_nlp["after_creation"] after_pipeline_creation = resolved_nlp["after_pipeline_creation"] @@ -1816,7 +1827,12 @@ class Language: # inside stuff like the spacy train function. If we loaded them here, # then we would load them twice at runtime: once when we make from config, # and then again when we load from disk. - nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer, meta=meta) + nlp = lang_cls( + vocab=vocab, + create_tokenizer=create_tokenizer, + create_vectors=create_vectors, + meta=meta, + ) if after_creation is not None: nlp = after_creation(nlp) if not isinstance(nlp, cls): diff --git a/spacy/ml/staticvectors.py b/spacy/ml/staticvectors.py index b75240c5d..1a1b0a0ff 100644 --- a/spacy/ml/staticvectors.py +++ b/spacy/ml/staticvectors.py @@ -9,7 +9,7 @@ from thinc.util import partial from ..attrs import ORTH from ..errors import Errors, Warnings from ..tokens import Doc -from ..vectors import Mode +from ..vectors import Mode, Vectors from ..vocab import Vocab @@ -48,11 +48,14 @@ def forward( key_attr: int = getattr(vocab.vectors, "attr", ORTH) keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs]) W = cast(Floats2d, model.ops.as_contig(model.get_param("W"))) - if vocab.vectors.mode == Mode.default: + if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default: V = model.ops.asarray(vocab.vectors.data) rows = vocab.vectors.find(keys=keys) V = model.ops.as_contig(V[rows]) - elif vocab.vectors.mode == Mode.floret: + elif isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.floret: + V = vocab.vectors.get_batch(keys) + V = model.ops.as_contig(V) + elif hasattr(vocab.vectors, "get_batch"): V = vocab.vectors.get_batch(keys) V = model.ops.as_contig(V) else: @@ -61,7 +64,7 @@ def forward( vectors_data = model.ops.gemm(V, W, trans2=True) except ValueError: raise RuntimeError(Errors.E896) - if vocab.vectors.mode == Mode.default: + if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default: # Convert negative indices to 0-vectors # TODO: more options for UNK tokens vectors_data[rows < 0] = 0 diff --git a/spacy/schemas.py b/spacy/schemas.py index 22c25e99d..3404687e1 100644 --- a/spacy/schemas.py +++ b/spacy/schemas.py @@ -397,6 +397,7 @@ class ConfigSchemaNlp(BaseModel): after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed") after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed") batch_size: Optional[int] = Field(..., title="Default batch size") + vectors: Callable = Field(..., title="Vectors implementation") # fmt: on class Config: diff --git a/spacy/util.py b/spacy/util.py index a2a033cbc..1689ac827 100644 --- a/spacy/util.py +++ b/spacy/util.py @@ -118,6 +118,7 @@ class registry(thinc.registry): augmenters = catalogue.create("spacy", "augmenters", entry_points=True) loggers = catalogue.create("spacy", "loggers", entry_points=True) scorers = catalogue.create("spacy", "scorers", entry_points=True) + vectors = catalogue.create("spacy", "vectors", entry_points=True) # These are factories registered via third-party packages and the # spacy_factories entry point. This registry only exists so we can easily # load them via the entry points. The "true" factories are added via the diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx index a88f380f9..2817bcad4 100644 --- a/spacy/vectors.pyx +++ b/spacy/vectors.pyx @@ -1,3 +1,6 @@ +# cython: infer_types=True, profile=True, binding=True +from typing import Callable + from cython.operator cimport dereference as deref from libc.stdint cimport uint32_t, uint64_t from libcpp.set cimport set as cppset @@ -5,7 +8,8 @@ from murmurhash.mrmr cimport hash128_x64 import warnings from enum import Enum -from typing import cast +from pathlib import Path +from typing import TYPE_CHECKING, Union, cast import numpy import srsly @@ -21,6 +25,9 @@ from .attrs import IDS from .errors import Errors, Warnings from .strings import get_string_id +if TYPE_CHECKING: + from .vocab import Vocab # noqa: F401 # no-cython-lint + def unpickle_vectors(bytes_data): return Vectors().from_bytes(bytes_data) @@ -35,7 +42,71 @@ class Mode(str, Enum): return list(cls.__members__.keys()) -cdef class Vectors: +cdef class BaseVectors: + def __init__(self, *, strings=None): + # Make sure abstract BaseVectors is not instantiated. + if self.__class__ == BaseVectors: + raise TypeError( + Errors.E1046.format(cls_name=self.__class__.__name__) + ) + + def __getitem__(self, key): + raise NotImplementedError + + def __contains__(self, key): + raise NotImplementedError + + def is_full(self): + raise NotImplementedError + + def get_batch(self, keys): + raise NotImplementedError + + @property + def shape(self): + raise NotImplementedError + + def __len__(self): + raise NotImplementedError + + @property + def vectors_length(self): + raise NotImplementedError + + @property + def size(self): + raise NotImplementedError + + def add(self, key, *, vector=None): + raise NotImplementedError + + def to_ops(self, ops: Ops): + pass + + # add dummy methods for to_bytes, from_bytes, to_disk and from_disk to + # allow serialization + def to_bytes(self, **kwargs): + return b"" + + def from_bytes(self, data: bytes, **kwargs): + return self + + def to_disk(self, path: Union[str, Path], **kwargs): + return None + + def from_disk(self, path: Union[str, Path], **kwargs): + return self + + +@util.registry.vectors("spacy.Vectors.v1") +def create_mode_vectors() -> Callable[["Vocab"], BaseVectors]: + def vectors_factory(vocab: "Vocab") -> BaseVectors: + return Vectors(strings=vocab.strings) + + return vectors_factory + + +cdef class Vectors(BaseVectors): """Store, save and load word vectors. Vectors data is kept in the vectors.data attribute, which should be an diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx index d1edc8533..48e8fcb90 100644 --- a/spacy/vocab.pyx +++ b/spacy/vocab.pyx @@ -94,8 +94,9 @@ cdef class Vocab: return self._vectors def __set__(self, vectors): - for s in vectors.strings: - self.strings.add(s) + if hasattr(vectors, "strings"): + for s in vectors.strings: + self.strings.add(s) self._vectors = vectors self._vectors.strings = self.strings @@ -193,7 +194,7 @@ cdef class Vocab: lex = mem.alloc(1, sizeof(LexemeC)) lex.orth = self.strings.add(string) lex.length = len(string) - if self.vectors is not None: + if self.vectors is not None and hasattr(self.vectors, "key2row"): lex.id = self.vectors.key2row.get(lex.orth, OOV_RANK) else: lex.id = OOV_RANK @@ -289,12 +290,17 @@ cdef class Vocab: @property def vectors_length(self): - return self.vectors.shape[1] + if hasattr(self.vectors, "shape"): + return self.vectors.shape[1] + else: + return -1 def reset_vectors(self, *, width=None, shape=None): """Drop the current vector table. Because all vectors must be the same width, you have to call this to change the size of the vectors. """ + if not isinstance(self.vectors, Vectors): + raise ValueError(Errors.E849.format(method="reset_vectors", vectors_type=type(self.vectors))) if width is not None and shape is not None: raise ValueError(Errors.E065.format(width=width, shape=shape)) elif shape is not None: @@ -304,6 +310,8 @@ cdef class Vocab: self.vectors = Vectors(strings=self.strings, shape=(self.vectors.shape[0], width)) def deduplicate_vectors(self): + if not isinstance(self.vectors, Vectors): + raise ValueError(Errors.E849.format(method="deduplicate_vectors", vectors_type=type(self.vectors))) if self.vectors.mode != VectorsMode.default: raise ValueError(Errors.E858.format( mode=self.vectors.mode, @@ -357,6 +365,8 @@ cdef class Vocab: DOCS: https://spacy.io/api/vocab#prune_vectors """ + if not isinstance(self.vectors, Vectors): + raise ValueError(Errors.E849.format(method="prune_vectors", vectors_type=type(self.vectors))) if self.vectors.mode != VectorsMode.default: raise ValueError(Errors.E858.format( mode=self.vectors.mode, diff --git a/website/docs/api/basevectors.mdx b/website/docs/api/basevectors.mdx new file mode 100644 index 000000000..993b9a33e --- /dev/null +++ b/website/docs/api/basevectors.mdx @@ -0,0 +1,143 @@ +--- +title: BaseVectors +teaser: Abstract class for word vectors +tag: class +source: spacy/vectors.pyx +version: 3.7 +--- + +`BaseVectors` is an abstract class to support the development of custom vectors +implementations. + +For use in training with [`StaticVectors`](/api/architectures#staticvectors), +`get_batch` must be implemented. For improved performance, use efficient +batching in `get_batch` and implement `to_ops` to copy the vector data to the +current device. See an example custom implementation for +[BPEmb subword embeddings](/usage/embeddings-transformers#custom-vectors). + +## BaseVectors.\_\_init\_\_ {id="init",tag="method"} + +Create a new vector store. + +| Name | Description | +| -------------- | --------------------------------------------------------------------------------------------------------------------- | +| _keyword-only_ | | +| `strings` | The string store. A new string store is created if one is not provided. Defaults to `None`. ~~Optional[StringStore]~~ | + +## BaseVectors.\_\_getitem\_\_ {id="getitem",tag="method"} + +Get a vector by key. If the key is not found in the table, a `KeyError` should +be raised. + +| Name | Description | +| ----------- | ---------------------------------------------------------------- | +| `key` | The key to get the vector for. ~~Union[int, str]~~ | +| **RETURNS** | The vector for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~ | + +## BaseVectors.\_\_len\_\_ {id="len",tag="method"} + +Return the number of vectors in the table. + +| Name | Description | +| ----------- | ------------------------------------------- | +| **RETURNS** | The number of vectors in the table. ~~int~~ | + +## BaseVectors.\_\_contains\_\_ {id="contains",tag="method"} + +Check whether there is a vector entry for the given key. + +| Name | Description | +| ----------- | -------------------------------------------- | +| `key` | The key to check. ~~int~~ | +| **RETURNS** | Whether the key has a vector entry. ~~bool~~ | + +## BaseVectors.add {id="add",tag="method"} + +Add a key to the table, if possible. If no keys can be added, return `-1`. + +| Name | Description | +| ----------- | ----------------------------------------------------------------------------------- | +| `key` | The key to add. ~~Union[str, int]~~ | +| **RETURNS** | The row the vector was added to, or `-1` if the operation is not supported. ~~int~~ | + +## BaseVectors.shape {id="shape",tag="property"} + +Get `(rows, dims)` tuples of number of rows and number of dimensions in the +vector table. + +| Name | Description | +| ----------- | ------------------------------------------ | +| **RETURNS** | A `(rows, dims)` pair. ~~Tuple[int, int]~~ | + +## BaseVectors.size {id="size",tag="property"} + +The vector size, i.e. `rows * dims`. + +| Name | Description | +| ----------- | ------------------------ | +| **RETURNS** | The vector size. ~~int~~ | + +## BaseVectors.is_full {id="is_full",tag="property"} + +Whether the vectors table is full and no slots are available for new keys. + +| Name | Description | +| ----------- | ------------------------------------------- | +| **RETURNS** | Whether the vectors table is full. ~~bool~~ | + +## BaseVectors.get_batch {id="get_batch",tag="method",version="3.2"} + +Get the vectors for the provided keys efficiently as a batch. Required to use +the vectors with [`StaticVectors`](/api/architectures#StaticVectors) for +training. + +| Name | Description | +| ------ | --------------------------------------- | +| `keys` | The keys. ~~Iterable[Union[int, str]]~~ | + +## BaseVectors.to_ops {id="to_ops",tag="method"} + +Dummy method. Implement this to change the embedding matrix to use different +Thinc ops. + +| Name | Description | +| ----- | -------------------------------------------------------- | +| `ops` | The Thinc ops to switch the embedding matrix to. ~~Ops~~ | + +## BaseVectors.to_disk {id="to_disk",tag="method"} + +Dummy method to allow serialization. Implement to save vector data with the +pipeline. + +| Name | Description | +| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ | +| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ | + +## BaseVectors.from_disk {id="from_disk",tag="method"} + +Dummy method to allow serialization. Implement to load vector data from a saved +pipeline. + +| Name | Description | +| ----------- | ----------------------------------------------------------------------------------------------- | +| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ | +| **RETURNS** | The modified vectors object. ~~BaseVectors~~ | + +## BaseVectors.to_bytes {id="to_bytes",tag="method"} + +Dummy method to allow serialization. Implement to serialize vector data to a +binary string. + +| Name | Description | +| ----------- | ---------------------------------------------------- | +| **RETURNS** | The serialized form of the vectors object. ~~bytes~~ | + +## BaseVectors.from_bytes {id="from_bytes",tag="method"} + +Dummy method to allow serialization. Implement to load vector data from a binary +string. + +| Name | Description | +| ----------- | ----------------------------------- | +| `data` | The data to load from. ~~bytes~~ | +| **RETURNS** | The vectors object. ~~BaseVectors~~ | diff --git a/website/docs/api/vectors.mdx b/website/docs/api/vectors.mdx index fa4cd0c7a..0e92eb12b 100644 --- a/website/docs/api/vectors.mdx +++ b/website/docs/api/vectors.mdx @@ -297,10 +297,9 @@ The vector size, i.e. `rows * dims`. ## Vectors.is_full {id="is_full",tag="property"} -Whether the vectors table is full and has no slots are available for new keys. -If a table is full, it can be resized using -[`Vectors.resize`](/api/vectors#resize). In `floret` mode, the table is always -full and cannot be resized. +Whether the vectors table is full and no slots are available for new keys. If a +table is full, it can be resized using [`Vectors.resize`](/api/vectors#resize). +In `floret` mode, the table is always full and cannot be resized. > #### Example > @@ -441,7 +440,7 @@ Load state from a binary string. > #### Example > > ```python -> fron spacy.vectors import Vectors +> from spacy.vectors import Vectors > vectors_bytes = vectors.to_bytes() > new_vectors = Vectors(StringStore()) > new_vectors.from_bytes(vectors_bytes) diff --git a/website/docs/usage/embeddings-transformers.mdx b/website/docs/usage/embeddings-transformers.mdx index 5f1e5b817..2bd2856b6 100644 --- a/website/docs/usage/embeddings-transformers.mdx +++ b/website/docs/usage/embeddings-transformers.mdx @@ -632,6 +632,165 @@ def MyCustomVectors( ) ``` +#### Creating a custom vectors implementation {id="custom-vectors",version="3.7"} + +You can specify a custom registered vectors class under `[nlp.vectors]` in order +to use static vectors in formats other than the ones supported by +[`Vectors`](/api/vectors). Extend the abstract [`BaseVectors`](/api/basevectors) +class to implement your custom vectors. + +As an example, the following `BPEmbVectors` class implements support for +[BPEmb subword embeddings](https://bpemb.h-its.org/): + +```python +# requires: pip install bpemb +import warnings +from pathlib import Path +from typing import Callable, Optional, cast + +from bpemb import BPEmb +from thinc.api import Ops, get_current_ops +from thinc.backends import get_array_ops +from thinc.types import Floats2d + +from spacy.strings import StringStore +from spacy.util import registry +from spacy.vectors import BaseVectors +from spacy.vocab import Vocab + + +class BPEmbVectors(BaseVectors): + def __init__( + self, + *, + strings: Optional[StringStore] = None, + lang: Optional[str] = None, + vs: Optional[int] = None, + dim: Optional[int] = None, + cache_dir: Optional[Path] = None, + encode_extra_options: Optional[str] = None, + model_file: Optional[Path] = None, + emb_file: Optional[Path] = None, + ): + kwargs = {} + if lang is not None: + kwargs["lang"] = lang + if vs is not None: + kwargs["vs"] = vs + if dim is not None: + kwargs["dim"] = dim + if cache_dir is not None: + kwargs["cache_dir"] = cache_dir + if encode_extra_options is not None: + kwargs["encode_extra_options"] = encode_extra_options + if model_file is not None: + kwargs["model_file"] = model_file + if emb_file is not None: + kwargs["emb_file"] = emb_file + self.bpemb = BPEmb(**kwargs) + self.strings = strings + self.name = repr(self.bpemb) + self.n_keys = -1 + self.mode = "BPEmb" + self.to_ops(get_current_ops()) + + def __contains__(self, key): + return True + + def is_full(self): + return True + + def add(self, key, *, vector=None, row=None): + warnings.warn( + ( + "Skipping BPEmbVectors.add: the bpemb vector table cannot be " + "modified. Vectors are calculated from bytepieces." + ) + ) + return -1 + + def __getitem__(self, key): + return self.get_batch([key])[0] + + def get_batch(self, keys): + keys = [self.strings.as_string(key) for key in keys] + bp_ids = self.bpemb.encode_ids(keys) + ops = get_array_ops(self.bpemb.emb.vectors) + indices = ops.asarray(ops.xp.hstack(bp_ids), dtype="int32") + lengths = ops.asarray([len(x) for x in bp_ids], dtype="int32") + vecs = ops.reduce_mean(cast(Floats2d, self.bpemb.emb.vectors[indices]), lengths) + return vecs + + @property + def shape(self): + return self.bpemb.vectors.shape + + def __len__(self): + return self.shape[0] + + @property + def vectors_length(self): + return self.shape[1] + + @property + def size(self): + return self.bpemb.vectors.size + + def to_ops(self, ops: Ops): + self.bpemb.emb.vectors = ops.asarray(self.bpemb.emb.vectors) + + +@registry.vectors("BPEmbVectors.v1") +def create_bpemb_vectors( + lang: Optional[str] = "multi", + vs: Optional[int] = None, + dim: Optional[int] = None, + cache_dir: Optional[Path] = None, + encode_extra_options: Optional[str] = None, + model_file: Optional[Path] = None, + emb_file: Optional[Path] = None, +) -> Callable[[Vocab], BPEmbVectors]: + def bpemb_vectors_factory(vocab: Vocab) -> BPEmbVectors: + return BPEmbVectors( + strings=vocab.strings, + lang=lang, + vs=vs, + dim=dim, + cache_dir=cache_dir, + encode_extra_options=encode_extra_options, + model_file=model_file, + emb_file=emb_file, + ) + + return bpemb_vectors_factory +``` + + + +Note that the serialization methods are not implemented, so the embeddings are +loaded from your local cache or downloaded by `BPEmb` each time the pipeline is +loaded. + + + +To use this in your pipeline, specify this registered function under +`[nlp.vectors]` in your config: + +```ini +[nlp.vectors] +@vectors = "BPEmbVectors.v1" +lang = "en" +``` + +Or specify it when creating a blank pipeline: + +```python +nlp = spacy.blank("en", config={"nlp.vectors": {"@vectors": "BPEmbVectors.v1", "lang": "en"}}) +``` + +Remember to include this code with `--code` when using +[`spacy train`](/api/cli#train) and [`spacy package`](/api/cli#package). + ## Pretraining {id="pretraining"} The [`spacy pretrain`](/api/cli#pretrain) command lets you initialize your diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json index 04102095f..d2f73d83a 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -131,6 +131,7 @@ "label": "Other", "items": [ { "text": "Attributes", "url": "/api/attributes" }, + { "text": "BaseVectors", "url": "/api/basevectors" }, { "text": "Corpus", "url": "/api/corpus" }, { "text": "InMemoryLookupKB", "url": "/api/inmemorylookupkb" }, { "text": "KnowledgeBase", "url": "/api/kb" }, From 222bd3c5b16968d7b6743b010d1bbbebb9b6df47 Mon Sep 17 00:00:00 2001 From: Madeesh Kannan Date: Wed, 2 Aug 2023 08:06:41 +0200 Subject: [PATCH 27/74] Display model's full base version string in incompatiblity warning (#12857) --- spacy/util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spacy/util.py b/spacy/util.py index 762699a97..79fcb8b8d 100644 --- a/spacy/util.py +++ b/spacy/util.py @@ -894,7 +894,7 @@ def load_meta(path: Union[str, Path]) -> Dict[str, Any]: if "spacy_version" in meta: if not is_compatible_version(about.__version__, meta["spacy_version"]): lower_version = get_model_lower_version(meta["spacy_version"]) - lower_version = get_minor_version(lower_version) # type: ignore[arg-type] + lower_version = get_base_version(lower_version) # type: ignore[arg-type] if lower_version is not None: lower_version = "v" + lower_version elif "spacy_git_version" in meta: From 07374430964848148bc018ff2f36f3dc3cf3b315 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Wed, 2 Aug 2023 08:15:12 +0200 Subject: [PATCH 28/74] feat: add example stubs (3) (#12801) * feat: add example stubs * fix: add required annotations * fix: mypy issues * fix: use Py36-compatible Portocol * Minor reformatting * adding further type specifications and removing internal methods * black formatting * widen type to iterable * add private methods that are being used by the built-in convertors * revert changes to corpus.py * fixes * fixes * fix typing of PlainTextCorpus --------- Co-authored-by: Basile Dura Co-authored-by: Adriane Boyd --- spacy/tokens/doc.pyi | 8 ++++- spacy/training/corpus.py | 2 +- spacy/training/example.pyi | 66 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 74 insertions(+), 2 deletions(-) create mode 100644 spacy/training/example.pyi diff --git a/spacy/tokens/doc.pyi b/spacy/tokens/doc.pyi index 00c7a9d07..55222f8aa 100644 --- a/spacy/tokens/doc.pyi +++ b/spacy/tokens/doc.pyi @@ -8,6 +8,7 @@ from typing import ( List, Optional, Protocol, + Sequence, Tuple, Union, overload, @@ -134,7 +135,12 @@ class Doc: def text(self) -> str: ... @property def text_with_ws(self) -> str: ... - ents: Tuple[Span] + # Ideally the getter would output Tuple[Span] + # see https://github.com/python/mypy/issues/3004 + @property + def ents(self) -> Sequence[Span]: ... + @ents.setter + def ents(self, value: Sequence[Span]) -> None: ... def set_ents( self, entities: List[Span], diff --git a/spacy/training/corpus.py b/spacy/training/corpus.py index 6037c15e3..5cc2733a5 100644 --- a/spacy/training/corpus.py +++ b/spacy/training/corpus.py @@ -63,7 +63,7 @@ def create_plain_text_reader( path: Optional[Path], min_length: int = 0, max_length: int = 0, -) -> Callable[["Language"], Iterable[Doc]]: +) -> Callable[["Language"], Iterable[Example]]: """Iterate Example objects from a file or directory of plain text UTF-8 files with one line per doc. diff --git a/spacy/training/example.pyi b/spacy/training/example.pyi new file mode 100644 index 000000000..06639d70c --- /dev/null +++ b/spacy/training/example.pyi @@ -0,0 +1,66 @@ +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple + +from ..tokens import Doc, Span +from ..vocab import Vocab +from .alignment import Alignment + +def annotations_to_doc( + vocab: Vocab, + tok_annot: Dict[str, Any], + doc_annot: Dict[str, Any], +) -> Doc: ... +def validate_examples( + examples: Iterable[Example], + method: str, +) -> None: ... +def validate_get_examples( + get_examples: Callable[[], Iterable[Example]], + method: str, +): ... + +class Example: + x: Doc + y: Doc + + def __init__( + self, + predicted: Doc, + reference: Doc, + *, + alignment: Optional[Alignment] = None, + ): ... + def __len__(self) -> int: ... + @property + def predicted(self) -> Doc: ... + @predicted.setter + def predicted(self, doc: Doc) -> None: ... + @property + def reference(self) -> Doc: ... + @reference.setter + def reference(self, doc: Doc) -> None: ... + def copy(self) -> Example: ... + @classmethod + def from_dict(cls, predicted: Doc, example_dict: Dict[str, Any]) -> Example: ... + @property + def alignment(self) -> Alignment: ... + def get_aligned(self, field: str, as_string=False): ... + def get_aligned_parse(self, projectivize=True): ... + def get_aligned_sent_starts(self): ... + def get_aligned_spans_x2y( + self, x_spans: Iterable[Span], allow_overlap=False + ) -> List[Span]: ... + def get_aligned_spans_y2x( + self, y_spans: Iterable[Span], allow_overlap=False + ) -> List[Span]: ... + def get_aligned_ents_and_ner(self) -> Tuple[List[Span], List[str]]: ... + def get_aligned_ner(self) -> List[str]: ... + def get_matching_ents(self, check_label: bool = True) -> List[Span]: ... + def to_dict(self) -> Dict[str, Any]: ... + def split_sents(self) -> List[Example]: ... + @property + def text(self) -> str: ... + def __str__(self) -> str: ... + def __repr__(self) -> str: ... + +def _parse_example_dict_data(example_dict): ... +def _fix_legacy_dict_data(example_dict): ... From e5773e0c6940070f192eb9663a9fda2a2c989dae Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Wed, 2 Aug 2023 09:35:16 +0200 Subject: [PATCH 29/74] Extend to spacy-transformers v1.3.x (#12877) --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 116e40f2c..d19e5bc01 100644 --- a/setup.cfg +++ b/setup.cfg @@ -81,7 +81,7 @@ console_scripts = lookups = spacy_lookups_data>=1.0.3,<1.1.0 transformers = - spacy_transformers>=1.1.2,<1.3.0 + spacy_transformers>=1.1.2,<1.4.0 cuda = cupy>=5.0.0b4,<13.0.0 cuda80 = From 07407e07ab8de5864bb61c5fa6c857b3373922b6 Mon Sep 17 00:00:00 2001 From: Arman Mohammadi <45389988+arplusman@users.noreply.github.com> Date: Wed, 2 Aug 2023 18:22:26 +0330 Subject: [PATCH 30/74] fix the regular expression matching on the full text (#12883) There was a mistake in the regex pattern which caused not matching all the desired tokens. The problem was that when we use r string literal prefix to suppose a raw text, we should not use two backslashes to demonstrate a backslash. --- website/docs/usage/rule-based-matching.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/usage/rule-based-matching.mdx b/website/docs/usage/rule-based-matching.mdx index 39be5f47b..4f54415cb 100644 --- a/website/docs/usage/rule-based-matching.mdx +++ b/website/docs/usage/rule-based-matching.mdx @@ -311,7 +311,7 @@ import re nlp = spacy.load("en_core_web_sm") doc = nlp("The United States of America (USA) are commonly known as the United States (U.S. or US) or America.") -expression = r"[Uu](nited|\\.?) ?[Ss](tates|\\.?)" +expression = r"[Uu](nited|\.?) ?[Ss](tates|\.?)" for match in re.finditer(expression, doc.text): start, end = match.span() span = doc.char_span(start, end) From 3b7faf4f5ea2346a1d71ba1d4583119d1508ba72 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Thu, 3 Aug 2023 08:37:43 +0200 Subject: [PATCH 31/74] fix (#12881) --- website/docs/api/spanruler.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/api/spanruler.mdx b/website/docs/api/spanruler.mdx index d2d41f620..5889b1906 100644 --- a/website/docs/api/spanruler.mdx +++ b/website/docs/api/spanruler.mdx @@ -117,7 +117,7 @@ config. Any existing patterns are removed on initialization. > > [initialize.components.span_ruler.patterns] > @readers = "srsly.read_jsonl.v1" -> path = "corpus/span_ruler_patterns.jsonl +> path = "corpus/span_ruler_patterns.jsonl" > ``` | Name | Description | From 45af8a5dcfe8134ec7b7320393cbfc57b7c2b967 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 4 Aug 2023 10:52:41 +0200 Subject: [PATCH 32/74] Update br tags (#12882) * Fix displacy br tag * Prefer
, also update package CLI --- spacy/cli/package.py | 2 +- spacy/displacy/render.py | 4 ++-- spacy/tests/test_displacy.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/spacy/cli/package.py b/spacy/cli/package.py index 4545578e6..12f195be1 100644 --- a/spacy/cli/package.py +++ b/spacy/cli/package.py @@ -403,7 +403,7 @@ def _format_sources(data: Any) -> str: if author: result += " ({})".format(author) sources.append(result) - return "
".join(sources) + return "
".join(sources) def _format_accuracy(data: Dict[str, Any], exclude: List[str] = ["speed"]) -> str: diff --git a/spacy/displacy/render.py b/spacy/displacy/render.py index 758dc07d5..2ab41ccc2 100644 --- a/spacy/displacy/render.py +++ b/spacy/displacy/render.py @@ -567,7 +567,7 @@ class EntityRenderer: for i, fragment in enumerate(fragments): markup += escape_html(fragment) if len(fragments) > 1 and i != len(fragments) - 1: - markup += "
" + markup += "
" if self.ents is None or label.upper() in self.ents: color = self.colors.get(label.upper(), self.default_color) ent_settings = { @@ -585,7 +585,7 @@ class EntityRenderer: for i, fragment in enumerate(fragments): markup += escape_html(fragment) if len(fragments) > 1 and i != len(fragments) - 1: - markup += "
" + markup += "
" markup = TPL_ENTS.format(content=markup, dir=self.direction) if title: markup = TPL_TITLE.format(title=title) + markup diff --git a/spacy/tests/test_displacy.py b/spacy/tests/test_displacy.py index e9b5a9aba..12d903dca 100644 --- a/spacy/tests/test_displacy.py +++ b/spacy/tests/test_displacy.py @@ -113,7 +113,7 @@ def test_issue5838(): doc = nlp(sample_text) doc.ents = [Span(doc, 7, 8, label="test")] html = displacy.render(doc, style="ent") - found = html.count("
") + found = html.count("
") assert found == 4 From 245e2ddc2584ff14d0c4a2725e078accd26bd310 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 8 Aug 2023 11:27:28 +0200 Subject: [PATCH 33/74] Allow pydantic v2 using transitional v1 support (#12888) --- requirements.txt | 2 +- setup.cfg | 2 +- .../pipeline/_edit_tree_internals/schemas.py | 8 +++- spacy/schemas.py | 41 +++++++++++++------ spacy/tests/pipeline/test_initialize.py | 7 +++- spacy/tests/pipeline/test_pipe_factories.py | 7 +++- spacy/tests/test_misc.py | 7 +++- 7 files changed, 54 insertions(+), 20 deletions(-) diff --git a/requirements.txt b/requirements.txt index 4a131d18c..4bc7d3820 100644 --- a/requirements.txt +++ b/requirements.txt @@ -16,7 +16,7 @@ smart-open>=5.2.1,<7.0.0 numpy>=1.15.0 requests>=2.13.0,<3.0.0 tqdm>=4.38.0,<5.0.0 -pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0 +pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0 jinja2 langcodes>=3.2.0,<4.0.0 # Official Python utilities diff --git a/setup.cfg b/setup.cfg index 45734888f..05044ef5c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -58,7 +58,7 @@ install_requires = tqdm>=4.38.0,<5.0.0 numpy>=1.15.0 requests>=2.13.0,<3.0.0 - pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0 + pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0 jinja2 # Official Python utilities setuptools diff --git a/spacy/pipeline/_edit_tree_internals/schemas.py b/spacy/pipeline/_edit_tree_internals/schemas.py index 1e307b66c..89f2861ce 100644 --- a/spacy/pipeline/_edit_tree_internals/schemas.py +++ b/spacy/pipeline/_edit_tree_internals/schemas.py @@ -1,8 +1,12 @@ from collections import defaultdict from typing import Any, Dict, List, Union -from pydantic import BaseModel, Field, ValidationError -from pydantic.types import StrictBool, StrictInt, StrictStr +try: + from pydantic.v1 import BaseModel, Field, ValidationError + from pydantic.v1.types import StrictBool, StrictInt, StrictStr +except ImportError: + from pydantic import BaseModel, Field, ValidationError # type: ignore + from pydantic.types import StrictBool, StrictInt, StrictStr # type: ignore class MatchNodeSchema(BaseModel): diff --git a/spacy/schemas.py b/spacy/schemas.py index 22f45372c..525885456 100644 --- a/spacy/schemas.py +++ b/spacy/schemas.py @@ -16,19 +16,34 @@ from typing import ( Union, ) -from pydantic import ( - BaseModel, - ConstrainedStr, - Field, - StrictBool, - StrictFloat, - StrictInt, - StrictStr, - ValidationError, - create_model, - validator, -) -from pydantic.main import ModelMetaclass +try: + from pydantic.v1 import ( + BaseModel, + ConstrainedStr, + Field, + StrictBool, + StrictFloat, + StrictInt, + StrictStr, + ValidationError, + create_model, + validator, + ) + from pydantic.v1.main import ModelMetaclass +except ImportError: + from pydantic import ( # type: ignore + BaseModel, + ConstrainedStr, + Field, + StrictBool, + StrictFloat, + StrictInt, + StrictStr, + ValidationError, + create_model, + validator, + ) + from pydantic.main import ModelMetaclass # type: ignore from thinc.api import ConfigValidationError, Model, Optimizer from thinc.config import Promise diff --git a/spacy/tests/pipeline/test_initialize.py b/spacy/tests/pipeline/test_initialize.py index 6dd4114f1..9854b391e 100644 --- a/spacy/tests/pipeline/test_initialize.py +++ b/spacy/tests/pipeline/test_initialize.py @@ -1,5 +1,10 @@ import pytest -from pydantic import StrictBool + +try: + from pydantic.v1 import StrictBool +except ImportError: + from pydantic import StrictBool # type: ignore + from thinc.api import ConfigValidationError from spacy.lang.en import English diff --git a/spacy/tests/pipeline/test_pipe_factories.py b/spacy/tests/pipeline/test_pipe_factories.py index 0f1454b55..83b986784 100644 --- a/spacy/tests/pipeline/test_pipe_factories.py +++ b/spacy/tests/pipeline/test_pipe_factories.py @@ -1,5 +1,10 @@ import pytest -from pydantic import StrictInt, StrictStr + +try: + from pydantic.v1 import StrictInt, StrictStr +except ImportError: + from pydantic import StrictInt, StrictStr # type: ignore + from thinc.api import ConfigValidationError, Linear, Model import spacy diff --git a/spacy/tests/test_misc.py b/spacy/tests/test_misc.py index 438f458ec..704a40485 100644 --- a/spacy/tests/test_misc.py +++ b/spacy/tests/test_misc.py @@ -3,7 +3,12 @@ import os from pathlib import Path import pytest -from pydantic import ValidationError + +try: + from pydantic.v1 import ValidationError +except ImportError: + from pydantic import ValidationError # type: ignore + from thinc.api import ( Config, ConfigValidationError, From c4e378df97bb3d966228b637184b6d81a50d5441 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 8 Aug 2023 12:58:28 +0200 Subject: [PATCH 34/74] Update CuPy extras (#12890) * Add `cuda12x` for `cupy-cuda12x`. * Drop `cuda-autodetect` from quickstart, set default to `cuda11x` instead. --- setup.cfg | 2 ++ website/src/widgets/quickstart-install.js | 14 +++++++++----- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/setup.cfg b/setup.cfg index 05044ef5c..4aaf0271b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -111,6 +111,8 @@ cuda117 = cupy-cuda117>=5.0.0b4,<13.0.0 cuda11x = cupy-cuda11x>=11.0.0,<13.0.0 +cuda12x = + cupy-cuda12x>=11.5.0,<13.0.0 cuda-autodetect = cupy-wheel>=11.0.0,<13.0.0 apple = diff --git a/website/src/widgets/quickstart-install.js b/website/src/widgets/quickstart-install.js index b6c8b9b4c..43e3a0eeb 100644 --- a/website/src/widgets/quickstart-install.js +++ b/website/src/widgets/quickstart-install.js @@ -10,15 +10,19 @@ const DEFAULT_PLATFORM = 'x86' const DEFAULT_MODELS = ['en'] const DEFAULT_OPT = 'efficiency' const DEFAULT_HARDWARE = 'cpu' -const DEFAULT_CUDA = 'cuda-autodetect' +const DEFAULT_CUDA = 'cuda11x' const CUDA = { '8.0': 'cuda80', '9.0': 'cuda90', - 9.1: 'cuda91', - 9.2: 'cuda92', + '9.1': 'cuda91', + '9.2': 'cuda92', '10.0': 'cuda100', - 10.1: 'cuda101', - '10.2, 11.0+': 'cuda-autodetect', + '10.1': 'cuda101', + '10.2': 'cuda102', + '11.0': 'cuda110', + '11.1': 'cuda111', + '11.2-11.x': 'cuda11x', + '12.x': 'cuda12x', } const LANG_EXTRAS = ['ja'] // only for languages with models From 458bc5f45c5e371bdbef43d58d078436ee496e43 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 8 Aug 2023 15:04:13 +0200 Subject: [PATCH 35/74] Set version to v3.6.1 (#12892) --- spacy/about.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spacy/about.py b/spacy/about.py index cad6158da..0f8eee0ff 100644 --- a/spacy/about.py +++ b/spacy/about.py @@ -1,6 +1,6 @@ # fmt: off __title__ = "spacy" -__version__ = "3.6.0" +__version__ = "3.6.1" __download_url__ = "https://github.com/explosion/spacy-models/releases/download" __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json" __projects__ = "https://github.com/explosion/projects" From 060241a8d571023937fd8ca701479909a90782da Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Thu, 10 Aug 2023 11:42:09 +0200 Subject: [PATCH 36/74] Revert "Extend to spacy-transformers v1.3.x (#12877)" This reverts commit e5773e0c6940070f192eb9663a9fda2a2c989dae. --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index d19e5bc01..116e40f2c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -81,7 +81,7 @@ console_scripts = lookups = spacy_lookups_data>=1.0.3,<1.1.0 transformers = - spacy_transformers>=1.1.2,<1.4.0 + spacy_transformers>=1.1.2,<1.3.0 cuda = cupy>=5.0.0b4,<13.0.0 cuda80 = From 9622c11529a5b8b25617fb72584997ee94d906ff Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 11 Aug 2023 10:59:51 +0200 Subject: [PATCH 37/74] Extend to weasel v0.2 (#12902) --- requirements.txt | 2 +- setup.cfg | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 9e787a223..89fc248fc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,7 +12,7 @@ catalogue>=2.0.6,<2.1.0 typer>=0.3.0,<0.10.0 pathy>=0.10.0 smart-open>=5.2.1,<7.0.0 -weasel>=0.1.0,<0.2.0 +weasel>=0.1.0,<0.3.0 # Third party dependencies numpy>=1.15.0; python_version < "3.9" numpy>=1.19.0; python_version >= "3.9" diff --git a/setup.cfg b/setup.cfg index 116e40f2c..078b7d4bd 100644 --- a/setup.cfg +++ b/setup.cfg @@ -56,7 +56,7 @@ install_requires = wasabi>=0.9.1,<1.2.0 srsly>=2.4.3,<3.0.0 catalogue>=2.0.6,<2.1.0 - weasel>=0.1.0,<0.2.0 + weasel>=0.1.0,<0.3.0 # Third-party dependencies typer>=0.3.0,<0.10.0 pathy>=0.10.0 From 6a4aa43164229262f6770272deedebe9ffc45329 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 11 Aug 2023 13:05:46 +0200 Subject: [PATCH 38/74] Extend to thinc v8.2 (#12897) --- pyproject.toml | 2 +- requirements.txt | 2 +- setup.cfg | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c611c6c1c..336c0793c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ requires = [ "cymem>=2.0.2,<2.1.0", "preshed>=3.0.2,<3.1.0", "murmurhash>=0.28.0,<1.1.0", - "thinc>=8.1.8,<8.2.0", + "thinc>=8.1.8,<8.3.0", "numpy>=1.15.0; python_version < '3.9'", "numpy>=1.25.0; python_version >= '3.9'", ] diff --git a/requirements.txt b/requirements.txt index 89fc248fc..237c790b4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0 spacy-loggers>=1.0.0,<2.0.0 cymem>=2.0.2,<2.1.0 preshed>=3.0.2,<3.1.0 -thinc>=8.1.8,<8.2.0 +thinc>=8.1.8,<8.3.0 ml_datasets>=0.2.0,<0.3.0 murmurhash>=0.28.0,<1.1.0 wasabi>=0.9.1,<1.2.0 diff --git a/setup.cfg b/setup.cfg index 078b7d4bd..b01298a72 100644 --- a/setup.cfg +++ b/setup.cfg @@ -44,7 +44,7 @@ setup_requires = cymem>=2.0.2,<2.1.0 preshed>=3.0.2,<3.1.0 murmurhash>=0.28.0,<1.1.0 - thinc>=8.1.8,<8.2.0 + thinc>=8.1.8,<8.3.0 install_requires = # Our libraries spacy-legacy>=3.0.11,<3.1.0 @@ -52,7 +52,7 @@ install_requires = murmurhash>=0.28.0,<1.1.0 cymem>=2.0.2,<2.1.0 preshed>=3.0.2,<3.1.0 - thinc>=8.1.8,<8.2.0 + thinc>=8.1.8,<8.3.0 wasabi>=0.9.1,<1.2.0 srsly>=2.4.3,<3.0.0 catalogue>=2.0.6,<2.1.0 From d50b8d51e20f4c66ac111e94fdc589e98769c03d Mon Sep 17 00:00:00 2001 From: denizcodeyaa <141595121+denizcodeyaa@users.noreply.github.com> Date: Fri, 11 Aug 2023 09:38:06 -0400 Subject: [PATCH 39/74] Update examples.py (#12895) Add: example sentences to improve the Turkish model. Let's get the tr_web_core_sm out in the the world yaa --- spacy/lang/tr/examples.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/spacy/lang/tr/examples.py b/spacy/lang/tr/examples.py index dfb324a4e..c912c950d 100644 --- a/spacy/lang/tr/examples.py +++ b/spacy/lang/tr/examples.py @@ -15,4 +15,7 @@ sentences = [ "Türkiye'nin başkenti neresi?", "Bakanlar Kurulu 180 günlük eylem planını açıkladı.", "Merkez Bankası, beklentiler doğrultusunda faizlerde değişikliğe gitmedi.", + "Cemal Sureya kimdir?", + "Bunlari Biliyor muydunuz?", + "Altinoluk Turkiye haritasinin neresinde yer alir?", ] From 64b8ee2dbe07ad70321a87cc55b653ef335f5c66 Mon Sep 17 00:00:00 2001 From: William Mattingly <62964060+wjbmattingly@users.noreply.github.com> Date: Mon, 14 Aug 2023 17:44:14 +0300 Subject: [PATCH 40/74] Update universe.json (#12904) * Update universe.json added hobbit-spacy to the universe json * Update universe.json removed displacy from hobbit-spacy and added a default text. --- website/meta/universe.json | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/website/meta/universe.json b/website/meta/universe.json index 2ed8b4b41..ec380f847 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -4444,6 +4444,31 @@ }, "category": ["pipeline", "standalone", "scientific"], "tags": ["ner"] + }, + { + "id": "hobbit-spacy", + "title": "Hobbit spaCy", + "slogan": "NLP for Middle Earth", + "description": "Hobbit spaCy is a custom spaCy pipeline designed specifically for working with Middle Earth and texts from the world of J.R.R. Tolkien.", + "github": "wjbmattingly/hobbit-spacy", + "pip": "en-hobbit", + "code_example": [ + "import spacy", + "", + "nlp = spacy.load('en_hobbit')", + "doc = nlp('Frodo saw Glorfindel and Glóin; and in a corner alone Strider was sitting, clad in his old travel - worn clothes again')" + ], + "code_language": "python", + "thumb": "https://github.com/wjbmattingly/hobbit-spacy/blob/main/images/hobbit-thumbnail.png?raw=true", + "image": "https://github.com/wjbmattingly/hobbit-spacy/raw/main/images/hobbitspacy.png", + "author": "W.J.B. Mattingly", + "author_links": { + "twitter": "wjb_mattingly", + "github": "wjbmattingly", + "website": "https://wjbmattingly.com" + }, + "category": ["pipeline", "standalone"], + "tags": ["spans", "rules", "ner"] } ], From 76a9f9c6c6546ec50cb00fab70dbf5f8ac6e0929 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Wed, 16 Aug 2023 17:28:34 +0200 Subject: [PATCH 41/74] Docs: clarify abstract spacy.load examples (#12889) --- website/docs/api/top-level.mdx | 2 +- website/docs/usage/processing-pipelines.mdx | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/api/top-level.mdx b/website/docs/api/top-level.mdx index 37e86a4bc..9cdc0c8ab 100644 --- a/website/docs/api/top-level.mdx +++ b/website/docs/api/top-level.mdx @@ -68,7 +68,7 @@ weights, and returns it. cls = spacy.util.get_lang_class(lang) # 1. Get Language class, e.g. English nlp = cls() # 2. Initialize it for name in pipeline: - nlp.add_pipe(name) # 3. Add the component to the pipeline + nlp.add_pipe(name, config={...}) # 3. Add the component to the pipeline nlp.from_disk(data_path) # 4. Load in the binary data ``` diff --git a/website/docs/usage/processing-pipelines.mdx b/website/docs/usage/processing-pipelines.mdx index 307cb9dcb..6ec8a0513 100644 --- a/website/docs/usage/processing-pipelines.mdx +++ b/website/docs/usage/processing-pipelines.mdx @@ -244,7 +244,7 @@ tagging pipeline. This is also why the pipeline state is always held by the together and returns an instance of `Language` with a pipeline set and access to the binary data: -```python {title="spacy.load under the hood"} +```python {title="spacy.load under the hood (abstract example)"} lang = "en" pipeline = ["tok2vec", "tagger", "parser", "ner", "attribute_ruler", "lemmatizer"] data_path = "path/to/en_core_web_sm/en_core_web_sm-3.0.0" @@ -252,7 +252,7 @@ data_path = "path/to/en_core_web_sm/en_core_web_sm-3.0.0" cls = spacy.util.get_lang_class(lang) # 1. Get Language class, e.g. English nlp = cls() # 2. Initialize it for name in pipeline: - nlp.add_pipe(name) # 3. Add the component to the pipeline + nlp.add_pipe(name, config={...}) # 3. Add the component to the pipeline nlp.from_disk(data_path) # 4. Load in the binary data ``` From 198488ee86735f0f37310913e8dbe69d01371241 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Wed, 16 Aug 2023 17:36:53 +0200 Subject: [PATCH 42/74] Extend to weasel v0.3 (#12908) * Extend to weasel v0.3 * Clean up unused imports in test_cli --- requirements.txt | 2 +- setup.cfg | 2 +- spacy/tests/test_cli.py | 8 ++------ 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/requirements.txt b/requirements.txt index 237c790b4..b6cc542a5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,7 +12,7 @@ catalogue>=2.0.6,<2.1.0 typer>=0.3.0,<0.10.0 pathy>=0.10.0 smart-open>=5.2.1,<7.0.0 -weasel>=0.1.0,<0.3.0 +weasel>=0.1.0,<0.4.0 # Third party dependencies numpy>=1.15.0; python_version < "3.9" numpy>=1.19.0; python_version >= "3.9" diff --git a/setup.cfg b/setup.cfg index b01298a72..9a5388c80 100644 --- a/setup.cfg +++ b/setup.cfg @@ -56,7 +56,7 @@ install_requires = wasabi>=0.9.1,<1.2.0 srsly>=2.4.3,<3.0.0 catalogue>=2.0.6,<2.1.0 - weasel>=0.1.0,<0.3.0 + weasel>=0.1.0,<0.4.0 # Third-party dependencies typer>=0.3.0,<0.10.0 pathy>=0.10.0 diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py index 9b4f6851e..c107992ed 100644 --- a/spacy/tests/test_cli.py +++ b/spacy/tests/test_cli.py @@ -1,18 +1,14 @@ import math import os -import time from collections import Counter from pathlib import Path from typing import Any, Dict, List, Tuple -import numpy import pytest import srsly from click import NoSuchOption from packaging.specifiers import SpecifierSet -from thinc.api import Config, ConfigValidationError -from weasel.cli.remote_storage import RemoteStorage -from weasel.cli.run import _check_requirements +from thinc.api import Config import spacy from spacy import about @@ -39,7 +35,7 @@ from spacy.cli.validate import get_model_pkgs from spacy.lang.en import English from spacy.lang.nl import Dutch from spacy.language import Language -from spacy.schemas import RecommendationSchema, validate +from spacy.schemas import RecommendationSchema from spacy.tokens import Doc, DocBin from spacy.tokens.span import Span from spacy.training import Example, docs_to_json, offsets_to_biluo_tags From 6dd56868de3c5e8308ef2ad31d7b63e40a87fe01 Mon Sep 17 00:00:00 2001 From: Connor Brinton Date: Mon, 21 Aug 2023 04:52:32 -0400 Subject: [PATCH 43/74] =?UTF-8?q?=F0=9F=93=9D=20Fix=20formula=20for=20rece?= =?UTF-8?q?ptive=20field=20in=20docs=20(#12918)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit SpaCy's HashEmbedCNN layer performs convolutions over tokens to produce contextualized embeddings using a `MaxoutWindowEncoder` layer. These convolutions are implemented using Thinc's `expand_window` layer, which concatenates `window_size` neighboring sequence items on either side of the sequence item being processed. This is repeated across `depth` convolutional layers. For example, consider the sequence "ABCDE" and a `MaxoutWindowEncoder` layer with a context window of 1 and a depth of 2. We'll focus on the token "C". We can visually represent the contextual embedding produced for "C" as: ```mermaid flowchart LR A0(A0) B0(B0) C0(C0) D0(D0) E0(E0) B1(B1) C1(C1) D1(D1) C2(C2) A0 --> B1 B0 --> B1 C0 --> B1 B0 --> C1 C0 --> C1 D0 --> C1 C0 --> D1 D0 --> D1 E0 --> D1 B1 --> C2 C1 --> C2 D1 --> C2 ``` Described in words, this graph shows that before the first layer of the convolution, the "receptive field" centered at each token consists only of that same token. That is to say, that we have a receptive field of 1. The first layer of the convolution adds one neighboring token on either side to the receptive field. Since this is done on both sides, the receptive field increases by 2, giving the first layer a receptive field of 3. The second layer of the convolutions adds an _additional_ neighboring token on either side to the receptive field, giving a final receptive field of 5. However, this doesn't match the formula currently given in the docs, which read: > The receptive field of the CNN will be > `depth * (window_size * 2 + 1)`, so a 4-layer network with a window > size of `2` will be sensitive to 20 words at a time. Substituting in our depth of 2 and window size of 1, this formula gives us a receptive field of: ``` depth * (window_size * 2 + 1) = 2 * (1 * 2 + 1) = 2 * (2 + 1) = 2 * 3 = 6 ``` This not only doesn't match our computations from above, it's also an even number! This is suspicious, since the receptive field is supposed to be centered on a token, and not between tokens. Generally, this formula results in an even number for any even value of `depth`. The error in this formula is that the adjustment for the center token is multiplied by the depth, when it should occur only once. The corrected formula, `depth * window_size * 2 + 1`, gives the correct value for our small example from above: ``` depth * window_size * 2 + 1 = 2 * 1 * 2 + 1 = 4 + 1 = 5 ``` These changes update the docs to correct the receptive field formula and the example receptive field size. --- spacy/ml/models/tok2vec.py | 4 ++-- website/docs/api/architectures.mdx | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py index 2e9d21ef4..0edc89991 100644 --- a/spacy/ml/models/tok2vec.py +++ b/spacy/ml/models/tok2vec.py @@ -67,8 +67,8 @@ def build_hash_embed_cnn_tok2vec( are between 2 and 8. window_size (int): The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be - depth * (window_size * 2 + 1), so a 4-layer network with window_size of - 2 will be sensitive to 20 words at a time. Recommended value is 1. + depth * window_size * 2 + 1, so a 4-layer network with window_size of + 2 will be sensitive to 17 words at a time. Recommended value is 1. embed_size (int): The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between 2000 and 10000. diff --git a/website/docs/api/architectures.mdx b/website/docs/api/architectures.mdx index bab24f13b..a292194e9 100644 --- a/website/docs/api/architectures.mdx +++ b/website/docs/api/architectures.mdx @@ -83,7 +83,7 @@ consisting of a CNN and a layer-normalized maxout activation function. | `width` | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~ | | `depth` | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~ | | `embed_size` | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~ | -| `window_size` | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * (window_size * 2 + 1)`, so a 4-layer network with a window size of `2` will be sensitive to 20 words at a time. Recommended value is `1`. ~~int~~ | +| `window_size` | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * window_size * 2 + 1`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. ~~int~~ | | `maxout_pieces` | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~ | | `subword_features` | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~ | | `pretrained_vectors` | Whether to also use static vectors. ~~bool~~ | From 869cc4ab0b44da9455e772f40d59244fa9c6eb28 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Tue, 22 Aug 2023 09:03:35 +0200 Subject: [PATCH 44/74] warn when an unsupported/unknown key is given to the dependency matcher (#12928) --- spacy/errors.py | 1 + spacy/matcher/dependencymatcher.pyx | 8 ++++++++ spacy/tests/matcher/test_dependency_matcher.py | 5 +++++ 3 files changed, 14 insertions(+) diff --git a/spacy/errors.py b/spacy/errors.py index 14ec669a3..dac07f804 100644 --- a/spacy/errors.py +++ b/spacy/errors.py @@ -219,6 +219,7 @@ class Warnings(metaclass=ErrorsWithCodes): W125 = ("The StaticVectors key_attr is no longer used. To set a custom " "key attribute for vectors, configure it through Vectors(attr=) or " "'spacy init vectors --attr'") + W126 = ("These keys are unsupported: {unsupported}") class Errors(metaclass=ErrorsWithCodes): diff --git a/spacy/matcher/dependencymatcher.pyx b/spacy/matcher/dependencymatcher.pyx index 348e000ff..1f66d99b2 100644 --- a/spacy/matcher/dependencymatcher.pyx +++ b/spacy/matcher/dependencymatcher.pyx @@ -129,6 +129,7 @@ cdef class DependencyMatcher: else: required_keys = {"RIGHT_ID", "RIGHT_ATTRS", "REL_OP", "LEFT_ID"} relation_keys = set(relation.keys()) + # Identify required keys that have not been specified missing = required_keys - relation_keys if missing: missing_txt = ", ".join(list(missing)) @@ -136,6 +137,13 @@ cdef class DependencyMatcher: required=required_keys, missing=missing_txt )) + # Identify additional, unsupported keys + unsupported = relation_keys - required_keys + if unsupported: + unsupported_txt = ", ".join(list(unsupported)) + warnings.warn(Warnings.W126.format( + unsupported=unsupported_txt + )) if ( relation["RIGHT_ID"] in visited_nodes or relation["LEFT_ID"] not in visited_nodes diff --git a/spacy/tests/matcher/test_dependency_matcher.py b/spacy/tests/matcher/test_dependency_matcher.py index 44b3bb26b..be33f90cf 100644 --- a/spacy/tests/matcher/test_dependency_matcher.py +++ b/spacy/tests/matcher/test_dependency_matcher.py @@ -216,6 +216,11 @@ def test_dependency_matcher_pattern_validation(en_vocab): pattern2 = copy.deepcopy(pattern) pattern2[1]["RIGHT_ID"] = "fox" matcher.add("FOUNDED", [pattern2]) + # invalid key + with pytest.warns(UserWarning): + pattern2 = copy.deepcopy(pattern) + pattern2[1]["FOO"] = "BAR" + matcher.add("FOUNDED", [pattern2]) def test_dependency_matcher_callback(en_vocab, doc): From d8a32c1050d2acb4fd121968d7e8780aae0b1382 Mon Sep 17 00:00:00 2001 From: PD Hall <20580126+pdhall99@users.noreply.github.com> Date: Tue, 29 Aug 2023 10:10:58 +0100 Subject: [PATCH 45/74] docs: fix ngram_range_suggester max_size description (#12939) --- website/docs/api/spancategorizer.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/api/spancategorizer.mdx b/website/docs/api/spancategorizer.mdx index 2b63d31ce..bfe33dfb9 100644 --- a/website/docs/api/spancategorizer.mdx +++ b/website/docs/api/spancategorizer.mdx @@ -521,7 +521,7 @@ has two columns, indicating the start and end position. | Name | Description | | ----------- | ---------------------------------------------------------------------------- | | `min_size` | The minimal phrase lengths to suggest (inclusive). ~~[int]~~ | -| `max_size` | The maximal phrase lengths to suggest (exclusive). ~~[int]~~ | +| `max_size` | The maximal phrase lengths to suggest (inclusive). ~~[int]~~ | | **CREATES** | The suggester function. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ | ### spacy.preset_spans_suggester.v1 {id="preset_spans_suggester"} From c2303858e617f28f981db599afd3b05c8c824321 Mon Sep 17 00:00:00 2001 From: Vinit Ravishankar Date: Tue, 29 Aug 2023 17:52:16 +0200 Subject: [PATCH 46/74] Documentation for spacy-curated-transformers (#12677) * initial * initial documentation run * fix typo * Remove mentions of Torchscript and quantization Both are disabled in the initial release of `spacy-curated-transformers`. * Fix `piece_encoder` entries * Remove `spacy-transformers`-specific warning * Fix duplicate entries in tables * Doc fixes Co-authored-by: Sofie Van Landeghem * Remove type aliases * Fix copy-paste typo * Change `debug pieces` version tag to `3.7` * Set curated transformers API version to `3.7` * Fix transformer listener naming * Add docs for `init fill-config-transformer` * Update CLI command invocation syntax * Update intro section of the pipeline component docs * Fix source URL * Add a note to the architectures section about the `init fill-config-transformer` CLI command * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem * Update CLI command name, args * Remove hyphen from the `curated-transformers.mdx` filename * Fix links * Remove placeholder text * Add text to the model/tokenizer loader sections * Fill in the `DocTransformerOutput` section * Formatting fixes * Add curated transformer page to API docs sidebar * More formatting fixes * Remove TODO comment * Remove outdated info about default config * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem * Add link to HF model hub * `prettier` --------- Co-authored-by: Madeesh Kannan Co-authored-by: Sofie Van Landeghem --- website/docs/api/architectures.mdx | 280 ++++++++++++ website/docs/api/cli.mdx | 73 ++- website/docs/api/curatedtransformer.mdx | 572 ++++++++++++++++++++++++ website/meta/sidebars.json | 1 + 4 files changed, 919 insertions(+), 7 deletions(-) create mode 100644 website/docs/api/curatedtransformer.mdx diff --git a/website/docs/api/architectures.mdx b/website/docs/api/architectures.mdx index bab24f13b..2853d2512 100644 --- a/website/docs/api/architectures.mdx +++ b/website/docs/api/architectures.mdx @@ -481,6 +481,286 @@ The other arguments are shared between all versions. +## Curated Transformer architectures {id="curated-trf",source="https://github.com/explosion/spacy-curated-transformers/blob/main/spacy_curated_transformers/models/architectures.py"} + +The following architectures are provided by the package +[`spacy-curated-transformers`](https://github.com/explosion/spacy-curated-transformers). +See the [usage documentation](/usage/embeddings-transformers#transformers) for +how to integrate the architectures into your training config. + +When loading the model +[from the Hugging Face Hub](/api/curatedtransformer#hf_trfencoder_loader), the +model config's parameters must be same as the hyperparameters used by the +pre-trained model. The +[`init fill-curated-transformer`](/api/cli#init-fill-curated-transformer) CLI +command can be used to automatically fill in these values. + +### spacy-curated-transformers.AlbertTransformer.v1 + +Construct an ALBERT transformer model. + +| Name | Description | +| ------------------------------ | ---------------------------------------------------------------------------------------- | +| `vocab_size` | Vocabulary size. ~~int~~ | +| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ | +| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ | +| `attention_probs_dropout_prob` | Dropout probability of the self-attention layers. ~~float~~ | +| `embedding_width` | Width of the embedding representations. ~~int~~ | +| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ | +| `hidden_dropout_prob` | Dropout probability of the point-wise feed-forward and embedding layers. ~~float~~ | +| `hidden_width` | Width of the final representations. ~~int~~ | +| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ | +| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ | +| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ | +| `model_max_length` | Maximum length of model inputs. ~~int~~ | +| `num_attention_heads` | Number of self-attention heads. ~~int~~ | +| `num_hidden_groups` | Number of layer groups whose constituents share parameters. ~~int~~ | +| `num_hidden_layers` | Number of hidden layers. ~~int~~ | +| `padding_idx` | Index of the padding meta-token. ~~int~~ | +| `type_vocab_size` | Type vocabulary size. ~~int~~ | +| `mixed_precision` | Use mixed-precision training. ~~bool~~ | +| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ | +| **CREATES** | The model using the architecture ~~Model~~ | + +### spacy-curated-transformers.BertTransformer.v1 + +Construct a BERT transformer model. + +| Name | Description | +| ------------------------------ | ---------------------------------------------------------------------------------------- | +| `vocab_size` | Vocabulary size. ~~int~~ | +| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ | +| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ | +| `attention_probs_dropout_prob` | Dropout probability of the self-attention layers. ~~float~~ | +| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ | +| `hidden_dropout_prob` | Dropout probability of the point-wise feed-forward and embedding layers. ~~float~~ | +| `hidden_width` | Width of the final representations. ~~int~~ | +| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ | +| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ | +| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ | +| `model_max_length` | Maximum length of model inputs. ~~int~~ | +| `num_attention_heads` | Number of self-attention heads. ~~int~~ | +| `num_hidden_layers` | Number of hidden layers. ~~int~~ | +| `padding_idx` | Index of the padding meta-token. ~~int~~ | +| `type_vocab_size` | Type vocabulary size. ~~int~~ | +| `mixed_precision` | Use mixed-precision training. ~~bool~~ | +| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ | +| **CREATES** | The model using the architecture ~~Model~~ | + +### spacy-curated-transformers.CamembertTransformer.v1 + +Construct a CamemBERT transformer model. + +| Name | Description | +| ------------------------------ | ---------------------------------------------------------------------------------------- | +| `vocab_size` | Vocabulary size. ~~int~~ | +| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ | +| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ | +| `attention_probs_dropout_prob` | Dropout probability of the self-attention layers. ~~float~~ | +| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ | +| `hidden_dropout_prob` | Dropout probability of the point-wise feed-forward and embedding layers. ~~float~~ | +| `hidden_width` | Width of the final representations. ~~int~~ | +| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ | +| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ | +| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ | +| `model_max_length` | Maximum length of model inputs. ~~int~~ | +| `num_attention_heads` | Number of self-attention heads. ~~int~~ | +| `num_hidden_layers` | Number of hidden layers. ~~int~~ | +| `padding_idx` | Index of the padding meta-token. ~~int~~ | +| `type_vocab_size` | Type vocabulary size. ~~int~~ | +| `mixed_precision` | Use mixed-precision training. ~~bool~~ | +| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ | +| **CREATES** | The model using the architecture ~~Model~~ | + +### spacy-curated-transformers.RobertaTransformer.v1 + +Construct a RoBERTa transformer model. + +| Name | Description | +| ------------------------------ | ---------------------------------------------------------------------------------------- | +| `vocab_size` | Vocabulary size. ~~int~~ | +| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ | +| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ | +| `attention_probs_dropout_prob` | Dropout probability of the self-attention layers. ~~float~~ | +| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ | +| `hidden_dropout_prob` | Dropout probability of the point-wise feed-forward and embedding layers. ~~float~~ | +| `hidden_width` | Width of the final representations. ~~int~~ | +| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ | +| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ | +| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ | +| `model_max_length` | Maximum length of model inputs. ~~int~~ | +| `num_attention_heads` | Number of self-attention heads. ~~int~~ | +| `num_hidden_layers` | Number of hidden layers. ~~int~~ | +| `padding_idx` | Index of the padding meta-token. ~~int~~ | +| `type_vocab_size` | Type vocabulary size. ~~int~~ | +| `mixed_precision` | Use mixed-precision training. ~~bool~~ | +| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ | +| **CREATES** | The model using the architecture ~~Model~~ | + +### spacy-curated-transformers.XlmrTransformer.v1 + +Construct a XLM-RoBERTa transformer model. + +| Name | Description | +| ------------------------------ | ---------------------------------------------------------------------------------------- | +| `vocab_size` | Vocabulary size. ~~int~~ | +| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ | +| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ | +| `attention_probs_dropout_prob` | Dropout probability of the self-attention layers. ~~float~~ | +| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ | +| `hidden_dropout_prob` | Dropout probability of the point-wise feed-forward and embedding layers. ~~float~~ | +| `hidden_width` | Width of the final representations. ~~int~~ | +| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ | +| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ | +| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ | +| `model_max_length` | Maximum length of model inputs. ~~int~~ | +| `num_attention_heads` | Number of self-attention heads. ~~int~~ | +| `num_hidden_layers` | Number of hidden layers. ~~int~~ | +| `padding_idx` | Index of the padding meta-token. ~~int~~ | +| `type_vocab_size` | Type vocabulary size. ~~int~~ | +| `mixed_precision` | Use mixed-precision training. ~~bool~~ | +| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ | +| **CREATES** | The model using the architecture ~~Model~~ | + +### spacy-curated-transformers.ScalarWeight.v1 + +Construct a model that accepts a list of transformer layer outputs and returns a +weighted representation of the same. + +| Name | Description | +| -------------------- | ----------------------------------------------------------------------------- | +| `num_layers` | Number of transformer hidden layers. ~~int~~ | +| `dropout_prob` | Dropout probability. ~~float~~ | +| `mixed_precision` | Use mixed-precision training. ~~bool~~ | +| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ | +| **CREATES** | The model using the architecture ~~Model[ScalarWeightInT, ScalarWeightOutT]~~ | + +### spacy-curated-transformers.TransformerLayersListener.v1 + +Construct a listener layer that communicates with one or more upstream +Transformer components. This layer extracts the output of the last transformer +layer and performs pooling over the individual pieces of each `Doc` token, +returning their corresponding representations. The upstream name should either +be the wildcard string '\*', or the name of the Transformer component. + +In almost all cases, the wildcard string will suffice as there'll only be one +upstream Transformer component. But in certain situations, e.g: you have +disjoint datasets for certain tasks, or you'd like to use a pre-trained pipeline +but a downstream task requires its own token representations, you could end up +with more than one Transformer component in the pipeline. + +| Name | Description | +| --------------- | ---------------------------------------------------------------------------------------------------------------------- | +| `layers` | The number of layers produced by the upstream transformer component, excluding the embedding layer. ~~int~~ | +| `width` | The width of the vectors produced by the upstream transformer component. ~~int~~ | +| `pooling` | Model that is used to perform pooling over the piece representations. ~~Model~~ | +| `upstream_name` | A string to identify the 'upstream' Transformer component to communicate with. ~~str~~ | +| `grad_factor` | Factor to multiply gradients with. ~~float~~ | +| **CREATES** | A model that returns the relevant vectors from an upstream transformer component. ~~Model[List[Doc], List[Floats2d]]~~ | + +### spacy-curated-transformers.LastTransformerLayerListener.v1 + +Construct a listener layer that communicates with one or more upstream +Transformer components. This layer extracts the output of the last transformer +layer and performs pooling over the individual pieces of each Doc token, +returning their corresponding representations. The upstream name should either +be the wildcard string '\*', or the name of the Transformer component. + +In almost all cases, the wildcard string will suffice as there'll only be one +upstream Transformer component. But in certain situations, e.g: you have +disjoint datasets for certain tasks, or you'd like to use a pre-trained pipeline +but a downstream task requires its own token representations, you could end up +with more than one Transformer component in the pipeline. + +| Name | Description | +| --------------- | ---------------------------------------------------------------------------------------------------------------------- | +| `width` | The width of the vectors produced by the upstream transformer component. ~~int~~ | +| `pooling` | Model that is used to perform pooling over the piece representations. ~~Model~~ | +| `upstream_name` | A string to identify the 'upstream' Transformer component to communicate with. ~~str~~ | +| `grad_factor` | Factor to multiply gradients with. ~~float~~ | +| **CREATES** | A model that returns the relevant vectors from an upstream transformer component. ~~Model[List[Doc], List[Floats2d]]~~ | + +### spacy-curated-transformers.ScalarWeightingListener.v1 + +Construct a listener layer that communicates with one or more upstream +Transformer components. This layer calculates a weighted representation of all +transformer layer outputs and performs pooling over the individual pieces of +each Doc token, returning their corresponding representations. + +Requires its upstream Transformer components to return all layer outputs from +their models. The upstream name should either be the wildcard string '\*', or +the name of the Transformer component. + +In almost all cases, the wildcard string will suffice as there'll only be one +upstream Transformer component. But in certain situations, e.g: you have +disjoint datasets for certain tasks, or you'd like to use a pre-trained pipeline +but a downstream task requires its own token representations, you could end up +with more than one Transformer component in the pipeline. + +| Name | Description | +| --------------- | ---------------------------------------------------------------------------------------------------------------------- | +| `width` | The width of the vectors produced by the upstream transformer component. ~~int~~ | +| `weighting` | Model that is used to perform the weighting of the different layer outputs. ~~Model~~ | +| `pooling` | Model that is used to perform pooling over the piece representations. ~~Model~~ | +| `upstream_name` | A string to identify the 'upstream' Transformer component to communicate with. ~~str~~ | +| `grad_factor` | Factor to multiply gradients with. ~~float~~ | +| **CREATES** | A model that returns the relevant vectors from an upstream transformer component. ~~Model[List[Doc], List[Floats2d]]~~ | + +### spacy-curated-transformers.BertWordpieceEncoder.v1 + +Construct a WordPiece piece encoder model that accepts a list of token sequences +or documents and returns a corresponding list of piece identifiers. This encoder +also splits each token on punctuation characters, as expected by most BERT +models. + +This model must be separately initialized using an appropriate loader. + +### spacy-curated-transformers.ByteBpeEncoder.v1 + +Construct a Byte-BPE piece encoder model that accepts a list of token sequences +or documents and returns a corresponding list of piece identifiers. + +This model must be separately initialized using an appropriate loader. + +### spacy-curated-transformers.CamembertSentencepieceEncoder.v1 + +Construct a SentencePiece piece encoder model that accepts a list of token +sequences or documents and returns a corresponding list of piece identifiers +with CamemBERT post-processing applied. + +This model must be separately initialized using an appropriate loader. + +### spacy-curated-transformers.CharEncoder.v1 + +Construct a character piece encoder model that accepts a list of token sequences +or documents and returns a corresponding list of piece identifiers. + +This model must be separately initialized using an appropriate loader. + +### spacy-curated-transformers.SentencepieceEncoder.v1 + +Construct a SentencePiece piece encoder model that accepts a list of token +sequences or documents and returns a corresponding list of piece identifiers. + +This model must be separately initialized using an appropriate loader. + +### spacy-curated-transformers.WordpieceEncoder.v1 + +Construct a WordPiece piece encoder model that accepts a list of token sequences +or documents and returns a corresponding list of piece identifiers. This encoder +also splits each token on punctuation characters, as expected by most BERT +models. + +This model must be separately initialized using an appropriate loader. + +### spacy-curated-transformers.XlmrSentencepieceEncoder.v1 + +Construct a SentencePiece piece encoder model that accepts a list of token +sequences or documents and returns a corresponding list of piece identifiers +with XLM-RoBERTa post-processing applied. + +This model must be separately initialized using an appropriate loader. + ## Pretraining architectures {id="pretrain",source="spacy/ml/models/multi_task.py"} The spacy `pretrain` command lets you initialize a `Tok2Vec` layer in your diff --git a/website/docs/api/cli.mdx b/website/docs/api/cli.mdx index 6a87f78b8..f71b7a75a 100644 --- a/website/docs/api/cli.mdx +++ b/website/docs/api/cli.mdx @@ -185,6 +185,29 @@ $ python -m spacy init fill-config [base_path] [output_file] [--diff] | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | | **CREATES** | Complete and auto-filled config file for training. | +### init fill-curated-transformer {id="init-fill-curated-transformer",version="3.7",tag="command"} + +Auto-fill the Hugging Face model hyperpameters and loader parameters of a +[Curated Transformer](/api/curatedtransformer) pipeline component in a +[.cfg file](/usage/training#config). The name and revision of the +[Hugging Face model](https://huggingface.co/models) can either be passed as +command-line arguments or read from the +`initialize.components.transformer.encoder_loader` config section. + +```bash +$ python -m spacy init fill-curated-transformer [base_path] [output_file] [--model-name] [--model-revision] [--pipe-name] [--code] +``` + +| Name | Description | +| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `base_path` | Path to base config to fill, e.g. generated by the [quickstart widget](/usage/training#quickstart). ~~Path (positional)~~ | +| `output_file` | Path to output `.cfg` file or "-" to write to stdout so you can pipe it to a file. Defaults to "-" (stdout). ~~Path (positional)~~ | +| `--model-name`, `-m` | Name of the Hugging Face model. Defaults to the model name from the encoder loader config. ~~Optional[str] (option)~~ | +| `--model-revision`, `-r` | Revision of the Hugging Face model. Defaults to `main`. ~~Optional[str] (option)~~ | +| `--pipe-name`, `-n` | Name of the Curated Transformer pipe whose config is to be filled. Defaults to the first transformer pipe. ~~Optional[str] (option)~~ | +| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ | +| **CREATES** | Complete and auto-filled config file for training. | + ### init vectors {id="init-vectors",version="3",tag="command"} Convert [word vectors](/usage/linguistic-features#vectors-similarity) for use @@ -1019,6 +1042,42 @@ $ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P | overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ | | **PRINTS** | Debugging information. | +### debug pieces {id="debug-pieces",version="3.7",tag="command"} + +Analyze word- or sentencepiece stats. + +```bash +$ python -m spacy debug pieces [config_path] [--code] [--name] [overrides] +``` + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `config_path` | Path to config file. ~~Union[Path, str] (positional)~~ | +| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ | +| `--name`, `-n` | Name of the Curated Transformer pipe whose config is to be filled. Defaults to the first transformer pipe. ~~Optional[str] (option)~~ | +| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ | +| **PRINTS** | Debugging information. | + + + +```bash +$ python -m spacy debug pieces ./config.cfg +``` + +``` +========================= Training corpus statistics ========================= +Median token length: 1.0 +Mean token length: 1.54 +Token length range: [1, 13] + +======================= Development corpus statistics ======================= +Median token length: 1.0 +Mean token length: 1.44 +Token length range: [1, 8] +``` + + + ## train {id="train",tag="command"} Train a pipeline. Expects data in spaCy's @@ -1651,10 +1710,10 @@ $ python -m spacy huggingface-hub push [whl_path] [--org] [--msg] [--verbose] > $ python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl > ``` -| Name | Description | -| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | -| `whl_path` | The path to the `.whl` file packaged with [`spacy package`](https://spacy.io/api/cli#package). ~~Path(positional)~~ | -| `--org`, `-o` | Optional name of organization to which the pipeline should be uploaded. ~~str (option)~~ | -| `--msg`, `-m` | Commit message to use for update. Defaults to `"Update spaCy pipeline"`. ~~str (option)~~ | -| `--verbose`, `-V` | Output additional info for debugging, e.g. the full generated hub metadata. ~~bool (flag)~~ | -| **UPLOADS** | The pipeline to the hub. | +| Name | Description | +| ----------------- | ------------------------------------------------------------------------------------------------------------------- | +| `whl_path` | The path to the `.whl` file packaged with [`spacy package`](https://spacy.io/api/cli#package). ~~Path(positional)~~ | +| `--org`, `-o` | Optional name of organization to which the pipeline should be uploaded. ~~str (option)~~ | +| `--msg`, `-m` | Commit message to use for update. Defaults to `"Update spaCy pipeline"`. ~~str (option)~~ | +| `--verbose`, `-V` | Output additional info for debugging, e.g. the full generated hub metadata. ~~bool (flag)~~ | +| **UPLOADS** | The pipeline to the hub. | diff --git a/website/docs/api/curatedtransformer.mdx b/website/docs/api/curatedtransformer.mdx new file mode 100644 index 000000000..5fdbd86cb --- /dev/null +++ b/website/docs/api/curatedtransformer.mdx @@ -0,0 +1,572 @@ +--- +title: CuratedTransformer +teaser: + Pipeline component for multi-task learning with Curated Transformer models +tag: class +source: github.com/explosion/spacy-curated-transformers/blob/main/spacy_curated_transformers/pipeline/transformer.py +version: 3.7 +api_base_class: /api/pipe +api_string_name: curated_transformer +--- + + + +This component is available via the extension package +[`spacy-curated-transformers`](https://github.com/explosion/spacy-curated-transformers). +It exposes the component via entry points, so if you have the package installed, +using `factory = "curated_transformer"` in your +[training config](/usage/training#config) will work out-of-the-box. + + + +This pipeline component lets you use a curated set of transformer models in your +pipeline. spaCy Curated Transformers currently supports the following model +types: + +- ALBERT +- BERT +- CamemBERT +- RoBERTa +- XLM-RoBERT + +If you want to use another type of model, use +[spacy-transformers](/api/spacy-transformers), which allows you to use all +Hugging Face transformer models with spaCy. + +You will usually connect downstream components to a shared Curated Transformer +pipe using one of the Curated Transformer listener layers. This works similarly +to spaCy's [Tok2Vec](/api/tok2vec), and the +[Tok2VecListener](/api/architectures/#Tok2VecListener) sublayer. The component +assigns the output of the transformer to the `Doc`'s extension attributes. To +access the values, you can use the custom +[`Doc._.trf_data`](#assigned-attributes) attribute. + +For more details, see the [usage documentation](/usage/embeddings-transformers). + +## Assigned Attributes {id="assigned-attributes"} + +The component sets the following +[custom extension attribute](/usage/processing-pipeline#custom-components-attributes): + +| Location | Value | +| ---------------- | -------------------------------------------------------------------------- | +| `Doc._.trf_data` | Curated Transformer outputs for the `Doc` object. ~~DocTransformerOutput~~ | + +## Config and Implementation {id="config"} + +The default config is defined by the pipeline component factory and describes +how the component should be configured. You can override its settings via the +`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your +[`config.cfg` for training](/usage/training#config). See the +[model architectures](/api/architectures#curated-trf) documentation for details +on the curated transformer architectures and their arguments and +hyperparameters. + +> #### Example +> +> ```python +> from spacy_curated_transformers.pipeline.transformer import DEFAULT_CONFIG +> +> nlp.add_pipe("curated_transformer", config=DEFAULT_CONFIG) +> ``` + +| Setting | Description | +| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Defaults to [`XlmrTransformer`](/api/architectures#curated-trf). ~~Model~~ | +| `frozen` | If `True`, the model's weights are frozen and no backpropagation is performed. ~~bool~~ | +| `all_layer_outputs` | If `True`, the model returns the outputs of all the layers. Otherwise, only the output of the last layer is returned. This must be set to `True` if any of the pipe's downstream listeners require the outputs of all transformer layers. ~~bool~~ | + +```python +https://github.com/explosion/spacy-curated-transformers/blob/main/spacy_curated_transformers/pipeline/transformer.py +``` + +## CuratedTransformer.\_\_init\_\_ {id="init",tag="method"} + +> #### Example +> +> ```python +> # Construction via add_pipe with default model +> trf = nlp.add_pipe("curated_transformer") +> +> # Construction via add_pipe with custom config +> config = { +> "model": { +> "@architectures": "spacy-curated-transformers.XlmrTransformer.v1", +> "vocab_size": 250002, +> "num_hidden_layers": 12, +> "hidden_width": 768, +> "piece_encoder": { +> "@architectures": "spacy-curated-transformers.XlmrSentencepieceEncoder.v1" +> } +> } +> } +> trf = nlp.add_pipe("curated_transformer", config=config) +> +> # Construction from class +> from spacy_curated_transformers import CuratedTransformer +> trf = CuratedTransformer(nlp.vocab, model) +> ``` + +Construct a `CuratedTransformer` component. One or more subsequent spaCy +components can use the transformer outputs as features in its model, with +gradients backpropagated to the single shared weights. The activations from the +transformer are saved in the [`Doc._.trf_data`](#assigned-attributes) extension +attribute. You can also provide a callback to set additional annotations. In +your application, you would normally use a shortcut for this and instantiate the +component using its string name and [`nlp.add_pipe`](/api/language#create_pipe). + +| Name | Description | +| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `vocab` | The shared vocabulary. ~~Vocab~~ | +| `model` | One of the supported pre-trained transformer models. ~~Model~~ | +| _keyword-only_ | | +| `name` | The component instance name. ~~str~~ | +| `frozen` | If `True`, the model's weights are frozen and no backpropagation is performed. ~~bool~~ | +| `all_layer_outputs` | If `True`, the model returns the outputs of all the layers. Otherwise, only the output of the last layer is returned. This must be set to `True` if any of the pipe's downstream listeners require the outputs of all transformer layers. ~~bool~~ | + +## CuratedTransformer.\_\_call\_\_ {id="call",tag="method"} + +Apply the pipe to one document. The document is modified in place, and returned. +This usually happens under the hood when the `nlp` object is called on a text +and all pipeline components are applied to the `Doc` in order. Both +[`__call__`](/api/curatedtransformer#call) and +[`pipe`](/api/curatedtransformer#pipe) delegate to the +[`predict`](/api/curatedtransformer#predict) and +[`set_annotations`](/api/curatedtransformer#set_annotations) methods. + +> #### Example +> +> ```python +> doc = nlp("This is a sentence.") +> trf = nlp.add_pipe("curated_transformer") +> # This usually happens under the hood +> processed = trf(doc) +> ``` + +| Name | Description | +| ----------- | -------------------------------- | +| `doc` | The document to process. ~~Doc~~ | +| **RETURNS** | The processed document. ~~Doc~~ | + +## CuratedTransformer.pipe {id="pipe",tag="method"} + +Apply the pipe to a stream of documents. This usually happens under the hood +when the `nlp` object is called on a text and all pipeline components are +applied to the `Doc` in order. Both [`__call__`](/api/curatedtransformer#call) +and [`pipe`](/api/curatedtransformer#pipe) delegate to the +[`predict`](/api/curatedtransformer#predict) and +[`set_annotations`](/api/curatedtransformer#set_annotations) methods. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> for doc in trf.pipe(docs, batch_size=50): +> pass +> ``` + +| Name | Description | +| -------------- | ------------------------------------------------------------- | +| `stream` | A stream of documents. ~~Iterable[Doc]~~ | +| _keyword-only_ | | +| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ | +| **YIELDS** | The processed documents in order. ~~Doc~~ | + +## CuratedTransformer.initialize {id="initialize",tag="method"} + +Initialize the component for training and return an +[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a +function that returns an iterable of [`Example`](/api/example) objects. **At +least one example should be supplied.** The data examples are used to +**initialize the model** of the component and can either be the full training +data or a representative sample. Initialization includes validating the network, +[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and +setting up the label scheme based on the data. This method is typically called +by [`Language.initialize`](/api/language#initialize). + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> trf.initialize(lambda: examples, nlp=nlp) +> ``` + +| Name | Description | +| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ | +| _keyword-only_ | | +| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | +| `encoder_loader` | Initialization callback for the transformer model. ~~Optional[Callable]~~ | +| `piece_loader` | Initialization callback for the input piece encoder. ~~Optional[Callable]~~ | + +## CuratedTransformer.predict {id="predict",tag="method"} + +Apply the component's model to a batch of [`Doc`](/api/doc) objects without +modifying them. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> scores = trf.predict([doc1, doc2]) +> ``` + +| Name | Description | +| ----------- | ------------------------------------------- | +| `docs` | The documents to predict. ~~Iterable[Doc]~~ | +| **RETURNS** | The model's prediction for each document. | + +## CuratedTransformer.set_annotations {id="set_annotations",tag="method"} + +Assign the extracted features to the `Doc` objects. By default, the +[`DocTransformerOutput`](/api/curatedtransformer#doctransformeroutput) object is +written to the [`Doc._.trf_data`](#assigned-attributes) attribute. Your +`set_extra_annotations` callback is then called, if provided. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> scores = trf.predict(docs) +> trf.set_annotations(docs, scores) +> ``` + +| Name | Description | +| -------- | ------------------------------------------------------------ | +| `docs` | The documents to modify. ~~Iterable[Doc]~~ | +| `scores` | The scores to set, produced by `CuratedTransformer.predict`. | + +## CuratedTransformer.update {id="update",tag="method"} + +Prepare for an update to the transformer. + +Like the [`Tok2Vec`](api/tok2vec) component, the `CuratedTransformer` component +is unusual in that it does not receive "gold standard" annotations to calculate +a weight update. The optimal output of the transformer data is unknown; it's a +hidden layer inside the network that is updated by backpropagating from output +layers. + +The `CuratedTransformer` component therefore does not perform a weight update +during its own `update` method. Instead, it runs its transformer model and +communicates the output and the backpropagation callback to any downstream +components that have been connected to it via the transformer listener sublayer. +If there are multiple listeners, the last layer will actually backprop to the +transformer and call the optimizer, while the others simply increment the +gradients. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> optimizer = nlp.initialize() +> losses = trf.update(examples, sgd=optimizer) +> ``` + +| Name | Description | +| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `examples` | A batch of [`Example`](/api/example) objects. Only the [`Example.predicted`](/api/example#predicted) `Doc` object is used, the reference `Doc` is ignored. ~~Iterable[Example]~~ | +| _keyword-only_ | | +| `drop` | The dropout rate. ~~float~~ | +| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ | +| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ | +| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ | + +## CuratedTransformer.create_optimizer {id="create_optimizer",tag="method"} + +Create an optimizer for the pipeline component. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> optimizer = trf.create_optimizer() +> ``` + +| Name | Description | +| ----------- | ---------------------------- | +| **RETURNS** | The optimizer. ~~Optimizer~~ | + +## CuratedTransformer.use_params {id="use_params",tag="method, contextmanager"} + +Modify the pipe's model to use the given parameter values. At the end of the +context, the original parameters are restored. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> with trf.use_params(optimizer.averages): +> trf.to_disk("/best_model") +> ``` + +| Name | Description | +| -------- | -------------------------------------------------- | +| `params` | The parameter values to use in the model. ~~dict~~ | + +## CuratedTransformer.to_disk {id="to_disk",tag="method"} + +Serialize the pipe to disk. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> trf.to_disk("/path/to/transformer") +> ``` + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | +| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | + +## CuratedTransformer.from_disk {id="from_disk",tag="method"} + +Load the pipe from disk. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> trf.from_disk("/path/to/transformer") +> ``` + +| Name | Description | +| -------------- | ----------------------------------------------------------------------------------------------- | +| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The modified `CuratedTransformer` object. ~~CuratedTransformer~~ | + +## CuratedTransformer.to_bytes {id="to_bytes",tag="method"} + +> #### Example +> +> ```python +> trf = nlp.add_pipe("curated_transformer") +> trf_bytes = trf.to_bytes() +> ``` + +Serialize the pipe to a bytestring. + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------- | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The serialized form of the `CuratedTransformer` object. ~~bytes~~ | + +## CuratedTransformer.from_bytes {id="from_bytes",tag="method"} + +Load the pipe from a bytestring. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> trf_bytes = trf.to_bytes() +> trf = nlp.add_pipe("curated_transformer") +> trf.from_bytes(trf_bytes) +> ``` + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------- | +| `bytes_data` | The data to load from. ~~bytes~~ | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The `CuratedTransformer` object. ~~CuratedTransformer~~ | + +## Serialization Fields {id="serialization-fields"} + +During serialization, spaCy will export several data fields used to restore +different aspects of the object. If needed, you can exclude them from +serialization by passing in the string names via the `exclude` argument. + +> #### Example +> +> ```python +> data = trf.to_disk("/path", exclude=["vocab"]) +> ``` + +| Name | Description | +| ------- | -------------------------------------------------------------- | +| `vocab` | The shared [`Vocab`](/api/vocab). | +| `cfg` | The config file. You usually don't want to exclude this. | +| `model` | The binary model data. You usually don't want to exclude this. | + +## DocTransformerOutput {id="doctransformeroutput",tag="dataclass"} + +Curated Transformer outputs for one `Doc` object. Stores the dense +representations generated by the transformer for each piece identifier. Piece +identifiers are grouped by token. Instances of this class are typically assigned +to the [`Doc._.trf_data`](/api/curatedtransformer#assigned-attributes) extension +attribute. + +| Name | Description | +| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `all_outputs` | List of `Ragged` tensors that correspends to outputs of the different transformer layers. Each tensor element corresponds to a piece identifier's representation. ~~List[Ragged]~~ | +| `last_layer_only` | If only the last transformer layer's outputs are preserved. ~~bool~~ | + +### DocTransformerOutput.embedding_layer {id="doctransformeroutput-embeddinglayer",tag="property"} + +Return the output of the transformer's embedding layer or `None` if +`last_layer_only` is `True`. + +| Name | Description | +| ----------- | -------------------------------------------- | +| **RETURNS** | Embedding layer output. ~~Optional[Ragged]~~ | + +### DocTransformerOutput.last_hidden_layer_state {id="doctransformeroutput-lasthiddenlayerstate",tag="property"} + +Return the output of the transformer's last hidden layer. + +| Name | Description | +| ----------- | ------------------------------------ | +| **RETURNS** | Last hidden layer output. ~~Ragged~~ | + +### DocTransformerOutput.all_hidden_layer_states {id="doctransformeroutput-allhiddenlayerstates",tag="property"} + +Return the outputs of all transformer layers (excluding the embedding layer). + +| Name | Description | +| ----------- | -------------------------------------- | +| **RETURNS** | Hidden layer outputs. ~~List[Ragged]~~ | + +### DocTransformerOutput.num_outputs {id="doctransformeroutput-numoutputs",tag="property"} + +Return the number of layer outputs stored in the `DocTransformerOutput` instance +(including the embedding layer). + +| Name | Description | +| ----------- | -------------------------- | +| **RETURNS** | Numbef of outputs. ~~int~~ | + +## Span Getters {id="span_getters",source="github.com/explosion/spacy-transformers/blob/master/spacy_curated_transformers/span_getters.py"} + +Span getters are functions that take a batch of [`Doc`](/api/doc) objects and +return a lists of [`Span`](/api/span) objects for each doc to be processed by +the transformer. This is used to manage long documents by cutting them into +smaller sequences before running the transformer. The spans are allowed to +overlap, and you can also omit sections of the `Doc` if they are not relevant. +Span getters can be referenced in the +`[components.transformer.model.with_spans]` block of the config to customize the +sequences processed by the transformer. + +| Name | Description | +| ----------- | ------------------------------------------------------------- | +| `docs` | A batch of `Doc` objects. ~~Iterable[Doc]~~ | +| **RETURNS** | The spans to process by the transformer. ~~List[List[Span]]~~ | + +### WithStridedSpans.v1 {id="strided_spans",tag="registered function"} + +> #### Example config +> +> ```ini +> [transformer.model.with_spans] +> @architectures = "spacy-curated-transformers.WithStridedSpans.v1" +> stride = 96 +> window = 128 +> ``` + +Create a span getter for strided spans. If you set the `window` and `stride` to +the same value, the spans will cover each token once. Setting `stride` lower +than `window` will allow for an overlap, so that some tokens are counted twice. +This can be desirable, because it allows all tokens to have both a left and +right context. + +| Name | Description | +| -------- | ------------------------ | +| `window` | The window size. ~~int~~ | +| `stride` | The stride size. ~~int~~ | + +## Model Loaders + +[Curated Transformer models](/api/architectures#curated-trf) are constructed +with default hyperparameters and randomized weights when the pipeline is +created. To load the weights of an existing pre-trained model into the pipeline, +one of the following loader callbacks can be used. The pre-trained model must +have the same hyperparameters as the model used by the pipeline. + +### HFTransformerEncoderLoader.v1 {id="hf_trfencoder_loader",tag="registered_function"} + +Construct a callback that initializes a supported transformer model with weights +from a corresponding HuggingFace model. + +| Name | Description | +| ---------- | ------------------------------------------ | +| `name` | Name of the HuggingFace model. ~~str~~ | +| `revision` | Name of the model revision/branch. ~~str~~ | + +### PyTorchCheckpointLoader.v1 {id="pytorch_checkpoint_loader",tag="registered_function"} + +Construct a callback that initializes a supported transformer model with weights +from a PyTorch checkpoint. + +| Name | Description | +| ------ | ---------------------------------------- | +| `path` | Path to the PyTorch checkpoint. ~~Path~~ | + +## Tokenizer Loaders + +[Curated Transformer models](/api/architectures#curated-trf) must be paired with +a matching tokenizer (piece encoder) model in a spaCy pipeline. As with the +transformer models, tokenizers are constructed with an empty vocabulary during +pipeline creation - They need to be initialized with an appropriate loader +before use in training/inference. + +### ByteBPELoader.v1 {id="bytebpe_loader",tag="registered_function"} + +Construct a callback that initializes a Byte-BPE piece encoder model. + +| Name | Description | +| ------------- | ------------------------------------- | +| `vocab_path` | Path to the vocabulary file. ~~Path~~ | +| `merges_path` | Path to the merges file. ~~Path~~ | + +### CharEncoderLoader.v1 {id="charencoder_loader",tag="registered_function"} + +Construct a callback that initializes a character piece encoder model. + +| Name | Description | +| ----------- | --------------------------------------------------------------------------- | +| `path` | Path to the serialized character model. ~~Path~~ | +| `bos_piece` | Piece used as a beginning-of-sentence token. Defaults to `"[BOS]"`. ~~str~~ | +| `eos_piece` | Piece used as a end-of-sentence token. Defaults to `"[EOS]"`. ~~str~~ | +| `unk_piece` | Piece used as a stand-in for unknown tokens. Defaults to `"[UNK]"`. ~~str~~ | +| `normalize` | Unicode normalization form to use. Defaults to `"NFKC"`. ~~str~~ | + +### HFPieceEncoderLoader.v1 {id="hf_pieceencoder_loader",tag="registered_function"} + +Construct a callback that initializes a HuggingFace piece encoder model. Used in +conjunction with the HuggingFace model loader. + +| Name | Description | +| ---------- | ------------------------------------------ | +| `name` | Name of the HuggingFace model. ~~str~~ | +| `revision` | Name of the model revision/branch. ~~str~~ | + +### SentencepieceLoader.v1 {id="sentencepiece_loader",tag="registered_function"} + +Construct a callback that initializes a SentencePiece piece encoder model. + +| Name | Description | +| ------ | ---------------------------------------------------- | +| `path` | Path to the serialized SentencePiece model. ~~Path~~ | + +### WordpieceLoader.v1 {id="wordpiece_loader",tag="registered_function"} + +Construct a callback that initializes a WordPiece piece encoder model. + +| Name | Description | +| ------ | ------------------------------------------------ | +| `path` | Path to the serialized WordPiece model. ~~Path~~ | + +## Callbacks + +### gradual_transformer_unfreezing.v1 {id="gradual_transformer_unfreezing",tag="registered_function"} + +Construct a callback that can be used to gradually unfreeze the weights of one +or more Transformer components during training. This can be used to prevent +catastrophic forgetting during fine-tuning. + +| Name | Description | +| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `target_pipes` | A dictionary whose keys and values correspond to the names of Transformer components and the training step at which they should be unfrozen respectively. ~~Dict[str, int]~~ | diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json index d2f73d83a..dd9a26af3 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -97,6 +97,7 @@ "items": [ { "text": "AttributeRuler", "url": "/api/attributeruler" }, { "text": "CoreferenceResolver", "url": "/api/coref" }, + { "text": "CuratedTransformer", "url": "/api/curatedtransformer" }, { "text": "DependencyParser", "url": "/api/dependencyparser" }, { "text": "EditTreeLemmatizer", "url": "/api/edittreelemmatizer" }, { "text": "EntityLinker", "url": "/api/entitylinker" }, From 52758e1afaa99b2ac47e0ae825f0a86d209952f4 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Wed, 30 Aug 2023 11:55:23 +0200 Subject: [PATCH 47/74] Add headers to netlify.toml [ci skip] --- website/netlify.toml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/website/netlify.toml b/website/netlify.toml index db7ae27c4..a99395918 100644 --- a/website/netlify.toml +++ b/website/netlify.toml @@ -16,3 +16,9 @@ NETLIFY_NEXT_PLUGIN_SKIP = "true" [[plugins]] package = "@netlify/plugin-nextjs" + +[[headers]] + for = "/*" + [headers.values] + X-Frame-Options = "DENY" + X-XSS-Protection = "1; mode=block" From 3e4264899c3b12f8eabc5cd700146177a34824d0 Mon Sep 17 00:00:00 2001 From: vincent d warmerdam Date: Wed, 30 Aug 2023 11:58:14 +0200 Subject: [PATCH 48/74] Update large-language-models.mdx (#12944) --- website/docs/api/large-language-models.mdx | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx index cc8328790..94b426cc8 100644 --- a/website/docs/api/large-language-models.mdx +++ b/website/docs/api/large-language-models.mdx @@ -893,7 +893,7 @@ OpenAI's `davinci` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Davinci.v1 " +> @llm_models = "spacy.Davinci.v1" > name = "davinci" > config = {"temperature": 0.3} > ``` @@ -914,7 +914,7 @@ OpenAI's `curie` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Curie.v1 " +> @llm_models = "spacy.Curie.v1" > name = "curie" > config = {"temperature": 0.3} > ``` @@ -935,7 +935,7 @@ OpenAI's `babbage` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Babbage.v1 " +> @llm_models = "spacy.Babbage.v1" > name = "babbage" > config = {"temperature": 0.3} > ``` @@ -956,7 +956,7 @@ OpenAI's `ada` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Ada.v1 " +> @llm_models = "spacy.Ada.v1" > name = "ada" > config = {"temperature": 0.3} > ``` @@ -977,7 +977,7 @@ Cohere's `command` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Command.v1 " +> @llm_models = "spacy.Command.v1" > name = "command" > config = {"temperature": 0.3} > ``` @@ -998,7 +998,7 @@ Anthropic's `claude-2` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Claude-2.v1 " +> @llm_models = "spacy.Claude-2.v1" > name = "claude-2" > config = {"temperature": 0.3} > ``` @@ -1019,7 +1019,7 @@ Anthropic's `claude-1` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Claude-1.v1 " +> @llm_models = "spacy.Claude-1.v1" > name = "claude-1" > config = {"temperature": 0.3} > ``` @@ -1040,7 +1040,7 @@ Anthropic's `claude-instant-1` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Claude-instant-1.v1 " +> @llm_models = "spacy.Claude-instant-1.v1" > name = "claude-instant-1" > config = {"temperature": 0.3} > ``` @@ -1061,7 +1061,7 @@ Anthropic's `claude-instant-1.1` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Claude-instant-1-1.v1 " +> @llm_models = "spacy.Claude-instant-1-1.v1" > name = "claude-instant-1.1" > config = {"temperature": 0.3} > ``` @@ -1082,7 +1082,7 @@ Anthropic's `claude-1.0` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Claude-1-0.v1 " +> @llm_models = "spacy.Claude-1-0.v1" > name = "claude-1.0" > config = {"temperature": 0.3} > ``` @@ -1124,7 +1124,7 @@ Anthropic's `claude-1.3` model family. > > ```ini > [components.llm.model] -> @llm_models = "spacy.Claude-1-3.v1 " +> @llm_models = "spacy.Claude-1-3.v1" > name = "claude-1.3" > config = {"temperature": 0.3} > ``` From 065ead4eed2608666c95dcad6037913e53fbf424 Mon Sep 17 00:00:00 2001 From: David Berenstein Date: Fri, 1 Sep 2023 11:05:36 +0200 Subject: [PATCH 49/74] updated `add_pipe` docs (#12947) --- website/meta/universe.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/meta/universe.json b/website/meta/universe.json index ec380f847..46de8121c 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -2806,7 +2806,7 @@ "", "# see github repo for examples on sentence-transformers and Huggingface", "nlp = spacy.load('en_core_web_md')", - "nlp.add_pipe(\"text_categorizer\", ", + "nlp.add_pipe(\"classy_classification\", ", " config={", " \"data\": data,", " \"model\": \"spacy\"", @@ -3010,8 +3010,8 @@ "# Load the spaCy language model:", "nlp = spacy.load(\"en_core_web_sm\")", "", - "# Add the \"text_categorizer\" pipeline component to the spaCy model, and configure it with SetFit parameters:", - "nlp.add_pipe(\"text_categorizer\", config={", + "# Add the \"spacy_setfit\" pipeline component to the spaCy model, and configure it with SetFit parameters:", + "nlp.add_pipe(\"spacy_setfit\", config={", " \"pretrained_model_name_or_path\": \"paraphrase-MiniLM-L3-v2\",", " \"setfit_trainer_args\": {", " \"train_dataset\": train_dataset", From 5c1f9264c219cfbd7b9c266c0bb8e00238f238c7 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Fri, 1 Sep 2023 13:47:20 +0200 Subject: [PATCH 50/74] fix typo in link (#12948) * fix typo in link * fix REL.v1 parameter --- website/docs/api/large-language-models.mdx | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx index 94b426cc8..e65945357 100644 --- a/website/docs/api/large-language-models.mdx +++ b/website/docs/api/large-language-models.mdx @@ -113,7 +113,7 @@ note that this requirement will be included in the prompt, but the task doesn't perform a hard cut-off. It's hence possible that your summary exceeds `max_n_words`. -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -192,7 +192,7 @@ the following parameters: span to the next token boundaries, e.g. expanding `"New Y"` out to `"New York"`. -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -282,7 +282,7 @@ the following parameters: span to the next token boundaries, e.g. expanding `"New Y"` out to `"New York"`. -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -397,7 +397,7 @@ definitions are included in the prompt. | `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -452,7 +452,7 @@ prompting and includes an improved prompt template. | `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -502,7 +502,7 @@ prompting. | `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Deafults to `True`. ~~bool~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~ | -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -546,12 +546,12 @@ on an upstream NER component for entities extraction. | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | | `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`rel.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.jinja). ~~str~~ | -| `label_description` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `label_definitions` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | | `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | | `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | | `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -565,6 +565,7 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`. [components.llm.task] @llm_tasks = "spacy.REL.v1" labels = ["LivesIn", "Visits"] + [components.llm.task.examples] @misc = "spacy.FewShotReader.v1" path = "rel_examples.jsonl" @@ -613,7 +614,7 @@ doesn't match the number of tokens from the pipeline's tokenizer, no lemmas are stored in the corresponding doc's tokens. Otherwise the tokens `.lemma_` property is updated with the lemma suggested by the LLM. -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. @@ -666,7 +667,7 @@ issues (e. g. in case of unexpected LLM responses) the value might be `None`. | `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | | `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ | -To perform [few-shot learning](/usage/large-langauge-models#few-shot-prompts), +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` supports `.yml`, `.yaml`, `.json` and `.jsonl`. From 6d1f6d9a23b4232e2ca67b5c2a15b62add8b5411 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Mon, 4 Sep 2023 09:05:50 +0200 Subject: [PATCH 51/74] Fix LLM usage example (#12950) * fix usage example * revert back to v2 to allow hot fix on main --- website/docs/usage/large-language-models.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/usage/large-language-models.mdx b/website/docs/usage/large-language-models.mdx index 3c2c52c68..4da9a8f16 100644 --- a/website/docs/usage/large-language-models.mdx +++ b/website/docs/usage/large-language-models.mdx @@ -184,7 +184,7 @@ nlp.add_pipe( "labels": ["PERSON", "ORGANISATION", "LOCATION"] }, "model": { - "@llm_models": "spacy.gpt-3.5.v1", + "@llm_models": "spacy.GPT-3-5.v1", }, }, ) From cc788476881ca456ddcb985675dc292fd75d5f40 Mon Sep 17 00:00:00 2001 From: Magdalena Aniol <96200718+magdaaniol@users.noreply.github.com> Date: Wed, 6 Sep 2023 16:38:13 +0200 Subject: [PATCH 52/74] fix training.batch_size example (#12963) --- website/docs/usage/training.mdx | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/website/docs/usage/training.mdx b/website/docs/usage/training.mdx index 98333db72..abb1b9cfd 100644 --- a/website/docs/usage/training.mdx +++ b/website/docs/usage/training.mdx @@ -180,7 +180,7 @@ Some of the main advantages and features of spaCy's training config are: Under the hood, the config is parsed into a dictionary. It's divided into sections and subsections, indicated by the square brackets and dot notation. For -example, `[training]` is a section and `[training.batch_size]` a subsection. +example, `[training]` is a section and `[training.batcher]` a subsection. Subsections can define values, just like a dictionary, or use the `@` syntax to refer to [registered functions](#config-functions). This allows the config to not just define static settings, but also construct objects like architectures, @@ -254,7 +254,7 @@ For cases like this, you can set additional command-line options starting with block. ```bash -$ python -m spacy train config.cfg --paths.train ./corpus/train.spacy --paths.dev ./corpus/dev.spacy --training.batch_size 128 +$ python -m spacy train config.cfg --paths.train ./corpus/train.spacy --paths.dev ./corpus/dev.spacy --training.max_epochs 3 ``` Only existing sections and values in the config can be overwritten. At the end @@ -279,7 +279,7 @@ process. Environment variables **take precedence** over CLI overrides and values defined in the config file. ```bash -$ SPACY_CONFIG_OVERRIDES="--system.gpu_allocator pytorch --training.batch_size 128" ./your_script.sh +$ SPACY_CONFIG_OVERRIDES="--system.gpu_allocator pytorch --training.max_epochs 3" ./your_script.sh ``` ### Reading from standard input {id="config-stdin"} @@ -578,16 +578,17 @@ now-updated model to the predicted docs. The training configuration defined in the config file doesn't have to only consist of static values. Some settings can also be **functions**. For instance, -the `batch_size` can be a number that doesn't change, or a schedule, like a +the batch size can be a number that doesn't change, or a schedule, like a sequence of compounding values, which has shown to be an effective trick (see [Smith et al., 2017](https://arxiv.org/abs/1711.00489)). ```ini {title="With static value"} -[training] -batch_size = 128 +[training.batcher] +@batchers = "spacy.batch_by_words.v1" +size = 3000 ``` -To refer to a function instead, you can make `[training.batch_size]` its own +To refer to a function instead, you can make `[training.batcher.size]` its own section and use the `@` syntax to specify the function and its arguments – in this case [`compounding.v1`](https://thinc.ai/docs/api-schedules#compounding) defined in the [function registry](/api/top-level#registry). All other values @@ -606,7 +607,7 @@ from your configs. > optimizer. ```ini {title="With registered function"} -[training.batch_size] +[training.batcher.size] @schedules = "compounding.v1" start = 100 stop = 1000 @@ -1027,14 +1028,14 @@ def my_custom_schedule(start: int = 1, factor: float = 1.001): ``` In your config, you can now reference the schedule in the -`[training.batch_size]` block via `@schedules`. If a block contains a key +`[training.batcher.size]` block via `@schedules`. If a block contains a key starting with an `@`, it's interpreted as a reference to a function. All other settings in the block will be passed to the function as keyword arguments. Keep in mind that the config shouldn't have any hidden defaults and all arguments on the functions need to be represented in the config. ```ini {title="config.cfg (excerpt)"} -[training.batch_size] +[training.batcher.size] @schedules = "my_custom_schedule.v1" start = 2 factor = 1.005 From def7013eec784347bdc37060f1a04f52f1d708e0 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Fri, 8 Sep 2023 10:25:14 +0200 Subject: [PATCH 53/74] Docs for spacy-llm 0.5.0 (#12968) * Update incorrect example config. (#12893) * spacy-llm docs cleanup (#12945) * Shorten NER section * fix template references * simplify sections * set temperature to 0.0 in examples * condense model information * fix parameters for REST models * set temperature to 0.0 * spelling fix * trigger preview * fix quotes * add small note on noop.v1 * move up example noop config * set appropriate model example configs * explain config * fix Co-authored-by: Raphael Mitsch --------- Co-authored-by: Raphael Mitsch * Docs for ner.v3 and spancat.v3 spacy-llm tasks (#12949) * formatting * update usage table with NER.v3 * fix typo in links * v3 overview of parameters * add spancat.v3 * add further v3 explanations * remove TODO comment * few more small fixes * Add doc section on LLM + task factories (#12905) * Add section on LLM + task factories. * Apply suggestions from code review --------- Co-authored-by: Sofie Van Landeghem * add default config to openai models (#12961) * Docs for spacy-llm 0.5.0 (#12967) * simplify Python example * simplify Python example * Refer only to latest OpenAI model versions from usage doc * Typo fix Co-authored-by: Raphael Mitsch * clarify accuracy claim --------- Co-authored-by: Raphael Mitsch --------- Co-authored-by: Raphael Mitsch --- website/docs/api/large-language-models.mdx | 1384 +++++++----------- website/docs/usage/large-language-models.mdx | 117 +- 2 files changed, 606 insertions(+), 895 deletions(-) diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx index e65945357..1ac9b0cef 100644 --- a/website/docs/api/large-language-models.mdx +++ b/website/docs/api/large-language-models.mdx @@ -2,7 +2,7 @@ title: Large Language Models teaser: Integrating LLMs into structured NLP pipelines menu: - - ['Config', 'config'] + - ['Config and implementation', 'config'] - ['Tasks', 'tasks'] - ['Models', 'models'] - ['Cache', 'cache'] @@ -14,45 +14,200 @@ Language Models (LLMs) into spaCy, featuring a modular system for **fast prototyping** and **prompting**, and turning unstructured responses into **robust outputs** for various NLP tasks, **no training data** required. -## Config {id="config"} +## Config and implementation {id="config"} -`spacy-llm` exposes a `llm` factory that accepts the following configuration -options: +An LLM component is implemented through the `LLMWrapper` class. It is accessible +through a generic `llm` +[component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories) +as well as through task-specific component factories: -| Argument | Description | -| ---------------- | ------------------------------------------------------------------------------------------------------- | -| `task` | An LLMTask can generate prompts and parse LLM responses. See [docs](#tasks). ~~Optional[LLMTask]~~ | -| `model` | Callable querying a specific LLM API. See [docs](#models). ~~Callable[[Iterable[Any]], Iterable[Any]]~~ | -| `cache` | Cache to use for caching prompts and responses per doc (batch). See [docs](#cache). ~~Cache~~ | -| `save_io` | Whether to save prompts/responses within `Doc.user_data["llm_io"]`. ~~bool~~ | -| `validate_types` | Whether to check if signatures of configured model and task are consistent. ~~bool~~ | +- `llm_ner` +- `llm_spancat` +- `llm_rel` +- `llm_textcat` +- `llm_sentiment` +- `llm_summarization` -An `llm` component is defined by two main settings: +### LLMWrapper.\_\_init\_\_ {id="init",tag="method"} -- A [**task**](#tasks), defining the prompt to send to the LLM as well as the - functionality to parse the resulting response back into structured fields on - the [Doc](/api/doc) objects. -- A [**model**](#models) defining the model and how to connect to it. Note that - `spacy-llm` supports both access to external APIs (such as OpenAI) as well as - access to self-hosted open-source LLMs (such as using Dolly through Hugging - Face). +> #### Example +> +> ```python +> # Construction via add_pipe with default GPT3.5 model and NER task +> config = {"task": {"@llm_tasks": "spacy.NER.v3", "labels": ["PERSON", "ORGANISATION", "LOCATION"]}} +> llm = nlp.add_pipe("llm") +> +> # Construction via add_pipe with task-specific factory and default GPT3.5 model +> parser = nlp.add_pipe("llm-ner", config=config) +> +> # Construction from class +> from spacy_llm.pipeline import LLMWrapper +> llm = LLMWrapper(vocab=nlp.vocab, task=task, model=model, cache=cache, save_io=True) +> ``` -Moreover, `spacy-llm` exposes a customizable [**caching**](#cache) functionality -to avoid running the same document through an LLM service (be it local or -through a REST API) more than once. +Create a new pipeline instance. In your application, you would normally use a +shortcut for this and instantiate the component using its string name and +[`nlp.add_pipe`](/api/language#add_pipe). -Finally, you can choose to save a stringified version of LLM prompts/responses -within the `Doc.user_data["llm_io"]` attribute by setting `save_io` to `True`. -`Doc.user_data["llm_io"]` is a dictionary containing one entry for every LLM -component within the `nlp` pipeline. Each entry is itself a dictionary, with two -keys: `prompt` and `response`. +| Name | Description | +| -------------- | -------------------------------------------------------------------------------------------------- | +| `name` | String name of the component instance. `llm` by default. ~~str~~ | +| _keyword-only_ | | +| `vocab` | The shared vocabulary. ~~Vocab~~ | +| `task` | An [LLM Task](#tasks) can generate prompts and parse LLM responses. ~~LLMTask~~ | +| `model` | The [LLM Model](#models) queries a specific LLM API.. ~~Callable[[Iterable[Any]], Iterable[Any]]~~ | +| `cache` | [Cache](#cache) to use for caching prompts and responses per doc. ~~Cache~~ | +| `save_io` | Whether to save LLM I/O (prompts and responses) in the `Doc._.llm_io` custom attribute. ~~bool~~ | -A note on `validate_types`: by default, `spacy-llm` checks whether the -signatures of the `model` and `task` callables are consistent with each other -and emits a warning if they don't. `validate_types` can be set to `False` if you -want to disable this behavior. +### LLMWrapper.\_\_call\_\_ {id="call",tag="method"} -### Tasks {id="tasks"} +Apply the pipe to one document. The document is modified in place and returned. +This usually happens under the hood when the `nlp` object is called on a text +and all pipeline components are applied to the `Doc` in order. + +> #### Example +> +> ```python +> doc = nlp("Ingrid visited Paris.") +> llm_ner = nlp.add_pipe("llm_ner") +> # This usually happens under the hood +> processed = llm_ner(doc) +> ``` + +| Name | Description | +| ----------- | -------------------------------- | +| `doc` | The document to process. ~~Doc~~ | +| **RETURNS** | The processed document. ~~Doc~~ | + +### LLMWrapper.pipe {id="pipe",tag="method"} + +Apply the pipe to a stream of documents. This usually happens under the hood +when the `nlp` object is called on a text and all pipeline components are +applied to the `Doc` in order. + +> #### Example +> +> ```python +> llm_ner = nlp.add_pipe("llm_ner") +> for doc in llm_ner.pipe(docs, batch_size=50): +> pass +> ``` + +| Name | Description | +| -------------- | ------------------------------------------------------------- | +| `docs` | A stream of documents. ~~Iterable[Doc]~~ | +| _keyword-only_ | | +| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ | +| **YIELDS** | The processed documents in order. ~~Doc~~ | + +### LLMWrapper.add_label {id="add_label",tag="method"} + +Add a new label to the pipe's task. Alternatively, provide the labels upon the +[task](#task) definition, or through the `[initialize]` block of the +[config](#config). + +> #### Example +> +> ```python +> llm_ner = nlp.add_pipe("llm_ner") +> llm_ner.add_label("MY_LABEL") +> ``` + +| Name | Description | +| ----------- | ----------------------------------------------------------- | +| `label` | The label to add. ~~str~~ | +| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ | + +### LLMWrapper.to_disk {id="to_disk",tag="method"} + +Serialize the pipe to disk. + +> #### Example +> +> ```python +> llm_ner = nlp.add_pipe("llm_ner") +> llm_ner.to_disk("/path/to/llm_ner") +> ``` + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | +| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | + +### LLMWrapper.from_disk {id="from_disk",tag="method"} + +Load the pipe from disk. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> llm_ner = nlp.add_pipe("llm_ner") +> llm_ner.from_disk("/path/to/llm_ner") +> ``` + +| Name | Description | +| -------------- | ----------------------------------------------------------------------------------------------- | +| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The modified `LLMWrapper` object. ~~LLMWrapper~~ | + +### LLMWrapper.to_bytes {id="to_bytes",tag="method"} + +> #### Example +> +> ```python +> llm_ner = nlp.add_pipe("llm_ner") +> ner_bytes = llm_ner.to_bytes() +> ``` + +Serialize the pipe to a bytestring. + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------- | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The serialized form of the `LLMWrapper` object. ~~bytes~~ | + +### LLMWrapper.from_bytes {id="from_bytes",tag="method"} + +Load the pipe from a bytestring. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> ner_bytes = llm_ner.to_bytes() +> llm_ner = nlp.add_pipe("llm_ner") +> llm_ner.from_bytes(ner_bytes) +> ``` + +| Name | Description | +| -------------- | ------------------------------------------------------------------------------------------- | +| `bytes_data` | The data to load from. ~~bytes~~ | +| _keyword-only_ | | +| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ | +| **RETURNS** | The `LLMWrapper` object. ~~LLMWrapper~~ | + +### LLMWrapper.labels {id="labels",tag="property"} + +The labels currently added to the component. Empty tuple if the LLM's task does +not require labels. + +> #### Example +> +> ```python +> llm_ner.add_label("MY_LABEL") +> assert "MY_LABEL" in llm_ner.labels +> ``` + +| Name | Description | +| ----------- | ------------------------------------------------------ | +| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ | + +## Tasks {id="tasks"} + +### Task implementation {id="task-implementation"} A _task_ defines an NLP problem or question, that will be sent to the LLM via a prompt. Further, the task defines how to parse the LLM's responses back into @@ -86,6 +241,11 @@ objects. This depends on the return type of the [model](#models). | `responses` | The generated prompts. ~~Iterable[Any]~~ | | **RETURNS** | The annotated documents. ~~Iterable[Doc]~~ | +### Summarization {id="summarization"} + +A summarization task takes a document as input and generates a summary that is +stored in an extension attribute. + #### spacy.Summarization.v1 {id="summarization-v1"} The `spacy.Summarization.v1` task supports both zero-shot and few-shot @@ -100,12 +260,12 @@ prompting. > max_n_words = null > ``` -| Argument | Description | -| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [summarization.jinja](./spacy_llm/tasks/templates/summarization.jinja). ~~str~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -| `max_n_words` | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~ | -| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~ | +| Argument | Description | +| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `template` | Custom prompt template to send to LLM model. Defaults to [summarization.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/summarization.v1.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `max_n_words` | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~ | +| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~ | The summarization task prompts the model for a concise summary of the provided text. It optionally allows to limit the response to a certain number of tokens - @@ -146,11 +306,111 @@ max_n_words = 20 path = "summarization_examples.yml" ``` +### NER {id="ner"} + +The NER task identifies non-overlapping entities in text. + +#### spacy.NER.v3 {id="ner-v3"} + +Version 3 is fundamentally different to v1 and v2, as it implements +Chain-of-Thought prompting, based on the +[PromptNER paper](https://arxiv.org/pdf/2305.15444.pdf) by Ashok and Lipton +(2023). On an internal use-case, we have found this implementation to obtain +significant better accuracy - with an increase of F-score of up to 15 percentage +points. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.NER.v3" +> labels = ["PERSON", "ORGANISATION", "LOCATION"] +> ``` + +When no examples are [specified](/usage/large-language-models#few-shot-prompts), +the v3 implementation will use a dummy example in the prompt. Technically this +means that the task will always perform few-shot prompting under the hood. + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `template` | Custom prompt template to send to LLM model. Defaults to [ner.v3.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v3.jinja). ~~str~~ | +| `description` (NEW) | A description of what to recognize or not recognize as entities. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | + +Note that the `single_match` parameter, used in v1 and v2, is not supported +anymore, as the CoT parsing algorithm takes care of this automatically. + +New to v3 is the fact that you can provide an explicit description of what +entities should look like. You can use this feature in addition to +`label_definitions`. + +```ini +[components.llm.task] +@llm_tasks = "spacy.NER.v3" +labels = ["DISH", "INGREDIENT", "EQUIPMENT"] +description = Entities are the names food dishes, + ingredients, and any kind of cooking equipment. + Adjectives, verbs, adverbs are not entities. + Pronouns are not entities. + +[components.llm.task.label_definitions] +DISH = "Known food dishes, e.g. Lobster Ravioli, garlic bread" +INGREDIENT = "Individual parts of a food dish, including herbs and spices." +EQUIPMENT = "Any kind of cooking equipment. e.g. oven, cooking pot, grill" +``` + +To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), +you can write down a few examples in a separate file, and provide these to be +injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` +supports `.yml`, `.yaml`, `.json` and `.jsonl`. + +While not required, this task works best when both positive and negative +examples are provided. The format is different than the files required for v1 +and v2, as additional fields such as `is_entity` and `reason` should now be +provided. + +```json +[ + { + "text": "You can't get a great chocolate flavor with carob.", + "spans": [ + { + "text": "chocolate", + "is_entity": false, + "label": "==NONE==", + "reason": "is a flavor in this context, not an ingredient" + }, + { + "text": "carob", + "is_entity": true, + "label": "INGREDIENT", + "reason": "is an ingredient to add chocolate flavor" + } + ] + }, + ... +] +``` + +```ini +[components.llm.task.examples] +@misc = "spacy.FewShotReader.v1" +path = "${paths.examples}" +``` + +For a fully working example, see this +[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_v3_openai). + #### spacy.NER.v2 {id="ner-v2"} -The built-in NER task supports both zero-shot and few-shot prompting. This -version also supports explicitly defining the provided labels with custom -descriptions. +This version supports explicitly defining the provided labels with custom +descriptions, and further supports zero-shot and few-shot prompting just like +v1. > #### Example config > @@ -161,84 +421,44 @@ descriptions. > examples = null > ``` -| Argument | Description | -| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ | -| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | -| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | -| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | -| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `template` (NEW) | Custom prompt template to send to LLM model. Defaults to [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | -The NER task implementation doesn't currently ask the LLM for specific offsets, -but simply expects a list of strings that represent the enties in the document. -This means that a form of string matching is required. This can be configured by -the following parameters: - -- The `single_match` parameter is typically set to `False` to allow for multiple - matches. For instance, the response from the LLM might only mention the entity - "Paris" once, but you'd still want to mark it every time it occurs in the - document. -- The case-sensitive matching is typically set to `False` to be robust against - case variances in the LLM's output. -- The `alignment_mode` argument is used to match entities as returned by the LLM - to the tokens from the original `Doc` - specifically it's used as argument in - the call to [`doc.char_span()`](/api/doc#char_span). The `"strict"` mode will - only keep spans that strictly adhere to the given token boundaries. - `"contract"` will only keep those tokens that are fully within the given - range, e.g. reducing `"New Y"` to `"New"`. Finally, `"expand"` will expand the - span to the next token boundaries, e.g. expanding `"New Y"` out to - `"New York"`. - -To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), -you can write down a few examples in a separate file, and provide these to be -injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` -supports `.yml`, `.yaml`, `.json` and `.jsonl`. - -```yaml -- text: Jack and Jill went up the hill. - entities: - PERSON: - - Jack - - Jill - LOCATION: - - hill -- text: Jack fell down and broke his crown. - entities: - PERSON: - - Jack -``` - -```ini -[components.llm.task] -@llm_tasks = "spacy.NER.v2" -labels = PERSON,ORGANISATION,LOCATION -[components.llm.task.examples] -@misc = "spacy.FewShotReader.v1" -path = "ner_examples.yml" -``` +The parameters `alignment_mode`, `case_sensitive_matching` and `single_match` +are identical to the [v1](#ner-v1) implementation. The format of few-shot +examples are also the same. > Label descriptions can also be used with explicit examples to give as much > info to the LLM model as possible. -You can also write definitions for each label and provide them via the -`label_definitions` argument. This lets you tell the LLM exactly what you're -looking for rather than relying on the LLM to interpret its task given just the -label name. Label descriptions are freeform so you can write whatever you want -here, but through some experiments a brief description along with some examples -and counter examples seems to work quite well. +New to v2 is the fact that you can write definitions for each label and provide +them via the `label_definitions` argument. This lets you tell the LLM exactly +what you're looking for rather than relying on the LLM to interpret its task +given just the label name. Label descriptions are freeform so you can write +whatever you want here, but a brief description along with some examples and +counter examples seems to work quite well. ```ini [components.llm.task] @llm_tasks = "spacy.NER.v2" labels = PERSON,SPORTS_TEAM + [components.llm.task.label_definitions] PERSON = "Extract any named individual in the text." SPORTS_TEAM = "Extract the names of any professional sports team. e.g. Golden State Warriors, LA Lakers, Man City, Real Madrid" ``` +For a fully working example, see this +[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/ner_dolly). + #### spacy.NER.v1 {id="ner-v1"} The original version of the built-in NER task supports both zero-shot and @@ -302,18 +522,48 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`. ``` ```ini -[components.llm.task] -@llm_tasks = "spacy.NER.v1" -labels = PERSON,ORGANISATION,LOCATION [components.llm.task.examples] @misc = "spacy.FewShotReader.v1" path = "ner_examples.yml" ``` +### SpanCat {id="spancat"} + +The SpanCat task identifies potentially overlapping entities in text. + +#### spacy.SpanCat.v3 {id="spancat-v3"} + +The built-in SpanCat v3 task is a simple adaptation of the NER v3 task to +support overlapping entities and store its annotations in `doc.spans`. + +> #### Example config +> +> ```ini +> [components.llm.task] +> @llm_tasks = "spacy.SpanCat.v3" +> labels = ["PERSON", "ORGANISATION", "LOCATION"] +> examples = null +> ``` + +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `template` | Custom prompt template to send to LLM model. Defaults to [`spancat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v3.jinja). ~~str~~ | +| `description` (NEW) | A description of what to recognize or not recognize as entities. ~~str~~ | +| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | + +Note that the `single_match` parameter, used in v1 and v2, is not supported +anymore, as the CoT parsing algorithm takes care of this automatically. + #### spacy.SpanCat.v2 {id="spancat-v2"} -The built-in SpanCat task is a simple adaptation of the NER task to support -overlapping entities and store its annotations in `doc.spans`. +The built-in SpanCat v2 task is a simple adaptation of the NER v2 task to +support overlapping entities and store its annotations in `doc.spans`. > #### Example config > @@ -324,20 +574,21 @@ overlapping entities and store its annotations in `doc.spans`. > examples = null > ``` -| Argument | Description | -| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ | -| `label_definitions` | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | -| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | -| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | -| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | -| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | +| Argument | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `template` (NEW) | Custom prompt template to send to LLM model. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~ | +| `spans_key` | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `alignment_mode` | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ | +| `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | +| `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | -Except for the `spans_key` parameter, the SpanCat task reuses the configuration -from the NER task. Refer to [its documentation](#ner-v2) for more insight. +Except for the `spans_key` parameter, the SpanCat v2 task reuses the +configuration from the NER v2 task. Refer to [its documentation](#ner-v2) for +more insight. #### spacy.SpanCat.v1 {id="spancat-v1"} @@ -364,14 +615,19 @@ v1 NER task to support overlapping entities and store its annotations in | `case_sensitive_matching` | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~ | | `single_match` | Whether to match an entity in the LLM's response only once (the first hit) or multiple times. Defaults to `False`. ~~bool~~ | -Except for the `spans_key` parameter, the SpanCat task reuses the configuration -from the NER task. Refer to [its documentation](#ner-v1) for more insight. +Except for the `spans_key` parameter, the SpanCat v1 task reuses the +configuration from the NER v1 task. Refer to [its documentation](#ner-v1) for +more insight. + +### TextCat {id="textcat"} + +The TextCat task labels documents with relevant categories. #### spacy.TextCat.v3 {id="textcat-v3"} -Version 3 (the most recent) of the built-in TextCat task supports both zero-shot -and few-shot prompting. It allows setting definitions of labels. Those -definitions are included in the prompt. +On top of the functionality from v2, version 3 of the built-in TextCat tasks +allows setting definitions of labels. Those definitions are included in the +prompt. > #### Example config > @@ -379,59 +635,30 @@ definitions are included in the prompt. > [components.llm.task] > @llm_tasks = "spacy.TextCat.v3" > labels = ["COMPLIMENT", "INSULT"] -> label_definitions = { -> "COMPLIMENT": "a polite expression of praise or admiration.", -> "INSULT": "a disrespectful or scornfully abusive remark or act." -> } +> +> [components.llm.task.label_definitions] +> "COMPLIMENT" = "a polite expression of praise or admiration.", +> "INSULT" = "a disrespectful or scornfully abusive remark or act." > examples = null > ``` -| Argument | Description | -| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | -| `label_definitions` | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | -| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | -| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | -| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | +| Argument | Description | +| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `label_definitions` (NEW) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `template` | Custom prompt template to send to LLM model. Defaults to [`textcat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v3.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | +| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | +| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), -you can write down a few examples in a separate file, and provide these to be -injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` -supports `.yml`, `.yaml`, `.json` and `.jsonl`. - -```json -[ - { - "text": "You look great!", - "answer": "Compliment" - }, - { - "text": "You are not very clever at all.", - "answer": "Insult" - } -] -``` - -```ini -[components.llm.task] -@llm_tasks = "spacy.TextCat.v3" -labels = ["COMPLIMENT", "INSULT"] -label_definitions = { - "COMPLIMENT": "a polite expression of praise or admiration.", - "INSULT": "a disrespectful or scornfully abusive remark or act." -} -[components.llm.task.examples] -@misc = "spacy.FewShotReader.v1" -path = "textcat_examples.json" -``` +The formatting of few-shot examples is the same as those for the +[v1](#textcat-v1) implementation. #### spacy.TextCat.v2 {id="textcat-v2"} -Version 2 of the built-in TextCat task supports both zero-shot and few-shot -prompting and includes an improved prompt template. +V2 includes all v1 functionality, with an improved prompt template. > #### Example config > @@ -442,42 +669,18 @@ prompting and includes an improved prompt template. > examples = null > ``` -| Argument | Description | -| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`textcat.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.jinja). ~~str~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | -| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | -| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | -| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` (NEW) | Custom prompt template to send to LLM model. Defaults to [`textcat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v2.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~ | +| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~ | +| `allow_none` | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ | +| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | -To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), -you can write down a few examples in a separate file, and provide these to be -injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1` -supports `.yml`, `.yaml`, `.json` and `.jsonl`. - -```json -[ - { - "text": "You look great!", - "answer": "Compliment" - }, - { - "text": "You are not very clever at all.", - "answer": "Insult" - } -] -``` - -```ini -[components.llm.task] -@llm_tasks = "spacy.TextCat.v2" -labels = ["COMPLIMENT", "INSULT"] -[components.llm.task.examples] -@misc = "spacy.FewShotReader.v1" -path = "textcat_examples.json" -``` +The formatting of few-shot examples is the same as those for the +[v1](#textcat-v1) implementation. #### spacy.TextCat.v1 {id="textcat-v1"} @@ -521,14 +724,15 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`. ``` ```ini -[components.llm.task] -@llm_tasks = "spacy.TextCat.v2" -labels = COMPLIMENT,INSULT [components.llm.task.examples] @misc = "spacy.FewShotReader.v1" path = "textcat_examples.json" ``` +### REL {id="rel"} + +The REL task extracts relations between named entities. + #### spacy.REL.v1 {id="rel-v1"} The built-in REL task supports both zero-shot and few-shot prompting. It relies @@ -542,14 +746,14 @@ on an upstream NER component for entities extraction. > labels = ["LivesIn", "Visits"] > ``` -| Argument | Description | -| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [`rel.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.jinja). ~~str~~ | -| `label_definitions` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | -| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | +| Argument | Description | +| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `labels` | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~ | +| `template` | Custom prompt template to send to LLM model. Defaults to [`rel.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.v1.jinja). ~~str~~ | +| `label_definitions` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `normalizer` | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ | +| `verbose` | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~ | To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be @@ -575,10 +779,17 @@ Note: the REL task relies on pre-extracted entities to make its prediction. Hence, you'll need to add a component that populates `doc.ents` with recognized spans to your spaCy pipeline and put it _before_ the REL component. +For a fully working example, see this +[usage example](https://github.com/explosion/spacy-llm/tree/main/usage_examples/rel_openai). + +### Lemma {id="lemma"} + +The Lemma task lemmatizes the provided text and updates the `lemma_` attribute +in the doc's tokens accordingly. + #### spacy.Lemma.v1 {id="lemma-v1"} -The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` -attribute in the doc's tokens accordingly. +This task supports both zero-shot and few-shot prompting. > #### Example config > @@ -588,14 +799,14 @@ attribute in the doc's tokens accordingly. > examples = null > ``` -| Argument | Description | -| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [lemma.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.jinja). ~~str~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| Argument | Description | +| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `template` | Custom prompt template to send to LLM model. Defaults to [lemma.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.v1.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -`Lemma.v1` prompts the LLM to lemmatize the passed text and return the -lemmatized version as a list of tokens and their corresponding lemma. E. g. the -text `I'm buying ice cream for my friends` should invoke the response +The task prompts the LLM to lemmatize the passed text and return the lemmatized +version as a list of tokens and their corresponding lemma. E. g. the text +`I'm buying ice cream for my friends` should invoke the response ``` I: I @@ -647,12 +858,16 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`. path = "lemma_examples.yml" ``` -#### spacy.Sentiment.v1 {id="sentiment-v1"} +### Sentiment {id="sentiment"} Performs sentiment analysis on provided texts. Scores between 0 and 1 are stored in `Doc._.sentiment` - the higher, the more positive. Note in cases of parsing issues (e. g. in case of unexpected LLM responses) the value might be `None`. +#### spacy.Sentiment.v1 {id="sentiment-v1"} + +This task supports both zero-shot and few-shot prompting. + > #### Example config > > ```ini @@ -661,11 +876,11 @@ issues (e. g. in case of unexpected LLM responses) the value might be `None`. > examples = null > ``` -| Argument | Description | -| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `template` | Custom prompt template to send to LLM model. Default templates for each task are located in the `spacy_llm/tasks/templates` directory. Defaults to [sentiment.jinja](./spacy_llm/tasks/templates/sentiment.jinja). ~~str~~ | -| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | -| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ | +| Argument | Description | +| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------ | +| `template` | Custom prompt template to send to LLM model. Defaults to [sentiment.v1.jinja](./spacy_llm/tasks/templates/sentiment.v1.jinja). ~~str~~ | +| `examples` | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~ | +| `field` | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~ | To perform [few-shot learning](/usage/large-language-models#few-shot-prompts), you can write down a few examples in a separate file, and provide these to be @@ -691,7 +906,10 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`. path = "sentiment_examples.yml" ``` -#### spacy.NoOp.v1 {id="noop-v1"} +### NoOp {id="noop"} + +This task is only useful for testing - it tells the LLM to do nothing, and does +not set any fields on the `docs`. > #### Example config > @@ -700,10 +918,11 @@ path = "sentiment_examples.yml" > @llm_tasks = "spacy.NoOp.v1" > ``` -This task is only useful for testing - it tells the LLM to do nothing, and does -not set any fields on the `docs`. +#### spacy.NoOp.v1 {id="noop-v1"} -### Models {id="models"} +This task needs no further configuration. + +## Models {id="models"} A _model_ defines which LLM model to query, and how to query it. It can be a simple function taking a collection of prompts (consistent with the output type @@ -713,6 +932,66 @@ it's a function of type `Callable[[Iterable[Any]], Iterable[Any]]`, but specific implementations can have other signatures, like `Callable[[Iterable[str]], Iterable[str]]`. +### Models via REST API {id="models-rest"} + +These models all take the same parameters, but note that the `config` should +contain provider-specific keys and values, as it will be passed onwards to the +provider's API. + +| Argument | Description | +| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. Default depends on the specific model (cf. below) ~~str~~ | +| `config` | Further configuration passed on to the model. Default depends on the specific model (cf. below). ~~Dict[Any, Any]~~ | +| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | +| `max_tries` | Max. number of tries for API request. Defaults to `5`. ~~int~~ | +| `max_request_time` | Max. time (in seconds) to wait for request to terminate before raising an exception. Defaults to `30.0`. ~~float~~ | +| `interval` | Time interval (in seconds) for API retries in seconds. Defaults to `1.0`. ~~float~~ | + +> #### Example config: +> +> ```ini +> [components.llm.model] +> @llm_models = "spacy.GPT-4.v1" +> name = "gpt-4" +> config = {"temperature": 0.0} +> ``` + +| Model | Provider | Supported names | Default name | Default config | +| ----------------------------- | --------- | ---------------------------------------------------------------------------------------- | ---------------------- | ------------------------------------ | +| `spacy.GPT-4.v1` | OpenAI | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]` | `"gpt-4"` | `{}` | +| `spacy.GPT-4.v2` | OpenAI | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]` | `"gpt-4"` | `{temperature=0.0}` | +| `spacy.GPT-3-5.v1` | OpenAI | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]` | `"gpt-3.5-turbo"` | `{}` | +| `spacy.GPT-3-5.v2` | OpenAI | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]` | `"gpt-3.5-turbo"` | `{temperature=0.0}` | +| `spacy.Davinci.v1` | OpenAI | `["davinci"]` | `"davinci"` | `{}` | +| `spacy.Davinci.v2` | OpenAI | `["davinci"]` | `"davinci"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Text-Davinci.v1` | OpenAI | `["text-davinci-003", "text-davinci-002"]` | `"text-davinci-003"` | `{}` | +| `spacy.Text-Davinci.v2` | OpenAI | `["text-davinci-003", "text-davinci-002"]` | `"text-davinci-003"` | `{temperature=0.0, max_tokens=1000}` | +| `spacy.Code-Davinci.v1` | OpenAI | `["code-davinci-002"]` | `"code-davinci-002"` | `{}` | +| `spacy.Code-Davinci.v2` | OpenAI | `["code-davinci-002"]` | `"code-davinci-002"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Curie.v1` | OpenAI | `["curie"]` | `"curie"` | `{}` | +| `spacy.Curie.v2` | OpenAI | `["curie"]` | `"curie"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Text-Curie.v1` | OpenAI | `["text-curie-001"]` | `"text-curie-001"` | `{}` | +| `spacy.Text-Curie.v2` | OpenAI | `["text-curie-001"]` | `"text-curie-001"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Babbage.v1` | OpenAI | `["babbage"]` | `"babbage"` | `{}` | +| `spacy.Babbage.v2` | OpenAI | `["babbage"]` | `"babbage"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Text-Babbage.v1` | OpenAI | `["text-babbage-001"]` | `"text-babbage-001"` | `{}` | +| `spacy.Text-Babbage.v2` | OpenAI | `["text-babbage-001"]` | `"text-babbage-001"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Ada.v1` | OpenAI | `["ada"]` | `"ada"` | `{}` | +| `spacy.Ada.v2` | OpenAI | `["ada"]` | `"ada"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Text-Ada.v1` | OpenAI | `["text-ada-001"]` | `"text-ada-001"` | `{}` | +| `spacy.Text-Ada.v2` | OpenAI | `["text-ada-001"]` | `"text-ada-001"` | `{temperature=0.0, max_tokens=500}` | +| `spacy.Command.v1` | Cohere | `["command", "command-light", "command-light-nightly", "command-nightly"]` | `"command"` | `{}` | +| `spacy.Claude-2.v1` | Anthropic | `["claude-2", "claude-2-100k"]` | `"claude-2"` | `{}` | +| `spacy.Claude-1.v1` | Anthropic | `["claude-1", "claude-1-100k"]` | `"claude-1"` | `{}` | +| `spacy.Claude-1-0.v1` | Anthropic | `["claude-1.0"]` | `"claude-1.0"` | `{}` | +| `spacy.Claude-1-2.v1` | Anthropic | `["claude-1.2"]` | `"claude-1.2"` | `{}` | +| `spacy.Claude-1-3.v1` | Anthropic | `["claude-1.3", "claude-1.3-100k"]` | `"claude-1.3"` | `{}` | +| `spacy.Claude-instant-1.v1` | Anthropic | `["claude-instant-1", "claude-instant-1-100k"]` | `"claude-instant-1"` | `{}` | +| `spacy.Claude-instant-1-1.v1` | Anthropic | `["claude-instant-1.1", "claude-instant-1.1-100k"]` | `"claude-instant-1.1"` | `{}` | + +To use these models, make sure that you've [set the relevant API](#api-keys) +keys as environment variables. + #### API Keys {id="api-keys"} Note that when using hosted services, you have to ensure that the proper API @@ -727,492 +1006,27 @@ export OPENAI_API_KEY="sk-..." export OPENAI_API_ORG="org-..." ``` -For Cohere it's +For Cohere: ```shell export CO_API_KEY="..." ``` -and for Anthropic +For Anthropic: ```shell export ANTHROPIC_API_KEY="..." ``` -#### spacy.GPT-4.v1 {id="gpt-4"} +### Models via HuggingFace {id="models-hf"} -OpenAI's `gpt-4` model family. +These models all take the same parameters: -> #### Example config: -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.GPT-4.v1" -> name = "gpt-4" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"gpt-4"`. ~~Literal["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.GPT-3-5.v1 {id="gpt-3-5"} - -OpenAI's `gpt-3-5` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.GPT-3-5.v1" -> name = "gpt-3.5-turbo" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"gpt-3.5-turbo"`. ~~Literal["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Text-Davinci.v1 {id="text-davinci"} - -OpenAI's `text-davinci` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Text-Davinci.v1" -> name = "text-davinci-003" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-davinci-003"`. ~~Literal["text-davinci-002", "text-davinci-003"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Code-Davinci.v1 {id="code-davinci"} - -OpenAI's `code-davinci` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Code-Davinci.v1" -> name = "code-davinci-002" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"code-davinci-002"`. ~~Literal["code-davinci-002"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Text-Curie.v1 {id="text-curie"} - -OpenAI's `text-curie` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Text-Curie.v1" -> name = "text-curie-001" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-curie-001"`. ~~Literal["text-curie-001"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Text-Babbage.v1 {id="text-babbage"} - -OpenAI's `text-babbage` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Text-Babbage.v1" -> name = "text-babbage-001" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-babbage-001"`. ~~Literal["text-babbage-001"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Text-Ada.v1 {id="text-ada"} - -OpenAI's `text-ada` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Text-Ada.v1" -> name = "text-ada-001" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"text-ada-001"`. ~~Literal["text-ada-001"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Davinci.v1 {id="davinci"} - -OpenAI's `davinci` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Davinci.v1" -> name = "davinci" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"davinci"`. ~~Literal["davinci"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Curie.v1 {id="curie"} - -OpenAI's `curie` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Curie.v1" -> name = "curie" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"curie"`. ~~Literal["curie"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Babbage.v1 {id="babbage"} - -OpenAI's `babbage` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Babbage.v1" -> name = "babbage" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"babbage"`. ~~Literal["babbage"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Ada.v1 {id="ada"} - -OpenAI's `ada` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Ada.v1" -> name = "ada" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"ada"`. ~~Literal["ada"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Command.v1 {id="command"} - -Cohere's `command` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Command.v1" -> name = "command" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"command"`. ~~Literal["command", "command-light", "command-light-nightly", "command-nightly"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Claude-2.v1 {id="claude-2"} - -Anthropic's `claude-2` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Claude-2.v1" -> name = "claude-2" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-2"`. ~~Literal["claude-2", "claude-2-100k"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Claude-1.v1 {id="claude-1"} - -Anthropic's `claude-1` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Claude-1.v1" -> name = "claude-1" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1"`. ~~Literal["claude-1", "claude-1-100k"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Claude-instant-1.v1 {id="claude-instant-1"} - -Anthropic's `claude-instant-1` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Claude-instant-1.v1" -> name = "claude-instant-1" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-instant-1"`. ~~Literal["claude-instant-1", "claude-instant-1-100k"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Claude-instant-1-1.v1 {id="claude-instant-1-1"} - -Anthropic's `claude-instant-1.1` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Claude-instant-1-1.v1" -> name = "claude-instant-1.1" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-instant-1.1"`. ~~Literal["claude-instant-1.1", "claude-instant-1.1-100k"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Claude-1-0.v1 {id="claude-1-0"} - -Anthropic's `claude-1.0` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Claude-1-0.v1" -> name = "claude-1.0" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.0"`. ~~Literal["claude-1.0"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Claude-1-2.v1 {id="claude-1-2"} - -Anthropic's `claude-1.2` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Claude-1-2.v1 " -> name = "claude-1.2" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.2"`. ~~Literal["claude-1.2"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Claude-1-3.v1 {id="claude-1-3"} - -Anthropic's `claude-1.3` model family. - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Claude-1-3.v1" -> name = "claude-1.3" -> config = {"temperature": 0.3} -> ``` - -| Argument | Description | -| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | Model name, i. e. any supported variant for this particular model. Defaults to `"claude-1.3"`. ~~Literal["claude-1.3", "claude-1.3-100k"]~~ | -| `config` | Further configuration passed on to the model. Defaults to `{}`. ~~Dict[Any, Any]~~ | -| `strict` | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ | -| `max_tries` | Max. number of tries for API request. Defaults to `3`. ~~int~~ | -| `timeout` | Timeout for API request in seconds. Defaults to `30`. ~~int~~ | - -#### spacy.Dolly.v1 {id="dolly"} - -To use this model, ideally you have a GPU enabled and have installed -`transformers`, `torch` and CUDA in your virtual environment. This allows you to -have the setting `device=cuda:0` in your config, which ensures that the model is -loaded entirely on the GPU (and fails otherwise). - -You can do so with - -```shell -python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" -``` - -If you don't have access to a GPU, you can install `accelerate` and -set`device_map=auto` instead, but be aware that this may result in some layers -getting distributed to the CPU or even the hard drive, which may ultimately -result in extremely slow queries. - -```shell -python -m pip install "accelerate>=0.16.0,<1.0" -``` - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Dolly.v1" -> name = "dolly-v2-3b" -> ``` - -| Argument | Description | -| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | The name of a Dolly model that is supported (e. g. "dolly-v2-3b" or "dolly-v2-12b"). ~~Literal["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]~~ | -| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | -| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | - -Supported models (see the -[Databricks models page](https://huggingface.co/databricks) on Hugging Face for -details): - -- `"databricks/dolly-v2-3b"` -- `"databricks/dolly-v2-7b"` -- `"databricks/dolly-v2-12b"` - -Note that Hugging Face will download this model the first time you use it - you -can -[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) -by setting the environmental variable `HF_HOME`. - -#### spacy.Llama2.v1 {id="llama2"} - -To use this model, ideally you have a GPU enabled and have installed -`transformers`, `torch` and CUDA in your virtual environment. This allows you to -have the setting `device=cuda:0` in your config, which ensures that the model is -loaded entirely on the GPU (and fails otherwise). - -You can do so with - -```shell -python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" -``` - -If you don't have access to a GPU, you can install `accelerate` and -set`device_map=auto` instead, but be aware that this may result in some layers -getting distributed to the CPU or even the hard drive, which may ultimately -result in extremely slow queries. - -```shell -python -m pip install "accelerate>=0.16.0,<1.0" -``` - -Note that the chat models variants of Llama 2 are currently not supported. This -is because they need a particular prompting setup and don't add any discernible -benefits in the use case of `spacy-llm` (i. e. no interactive chat) compared the -completion model variants. +| Argument | Description | +| ------------- | ------------------------------------------------------------------------------------------------------------------------------------- | +| `name` | Model name, i. e. any supported variant for this particular model. ~~str~~ | +| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | +| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | > #### Example config > @@ -1222,108 +1036,27 @@ completion model variants. > name = "llama2-7b-hf" > ``` -| Argument | Description | -| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | -| `name` | The name of a Llama 2 model variant that is supported. Defaults to `"Llama-2-7b-hf"`. ~~Literal["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]~~ | -| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | -| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | +| Model | Provider | Supported names | HF directory | +| -------------------- | --------------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------- | +| `spacy.Dolly.v1` | Databricks | `["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]` | https://huggingface.co/databricks | +| `spacy.Llama2.v1` | Meta AI | `["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]` | https://huggingface.co/meta-llama | +| `spacy.Falcon.v1` | TII | `["falcon-rw-1b", "falcon-7b", "falcon-7b-instruct", "falcon-40b-instruct"]` | https://huggingface.co/tiiuae | +| `spacy.StableLM.v1` | Stability AI | `["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]` | https://huggingface.co/stabilityai | +| `spacy.OpenLLaMA.v1` | OpenLM Research | `["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]` | https://huggingface.co/openlm-research | -Note that Hugging Face will download this model the first time you use it - you -can -[define the cache directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) -by setting the environmental variable `HF_HOME`. +See the "HF directory" for more details on each of the models. -#### spacy.Falcon.v1 {id="falcon"} - -To use this model, ideally you have a GPU enabled and have installed -`transformers`, `torch` and CUDA in your virtual environment. This allows you to -have the setting `device=cuda:0` in your config, which ensures that the model is -loaded entirely on the GPU (and fails otherwise). - -You can do so with - -```shell -python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" -``` - -If you don't have access to a GPU, you can install `accelerate` and -set`device_map=auto` instead, but be aware that this may result in some layers -getting distributed to the CPU or even the hard drive, which may ultimately -result in extremely slow queries. - -```shell -python -m pip install "accelerate>=0.16.0,<1.0" -``` - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.Falcon.v1" -> name = "falcon-7b" -> ``` - -| Argument | Description | -| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| `name` | The name of a Falcon model variant that is supported. Defaults to `"7b-instruct"`. ~~Literal["falcon-rw-1b", "falcon-7b", "falcon-7b-instruct", "falcon-40b-instruct"]~~ | -| `config_init` | Further configuration passed on to the construction of the model with `transformers.pipeline()`. Defaults to `{}`. ~~Dict[str, Any]~~ | -| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | - -Note that Hugging Face will download this model the first time you use it - you -can -[define the cache directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) -by setting the environmental variable `HF_HOME`. - -#### spacy.StableLM.v1 {id="stablelm"} - -To use this model, ideally you have a GPU enabled and have installed -`transformers`, `torch` and CUDA in your virtual environment. - -You can do so with - -```shell -python -m pip install "spacy-llm[transformers]" "transformers[sentencepiece]" -``` - -If you don't have access to a GPU, you can install `accelerate` and -set`device_map=auto` instead, but be aware that this may result in some layers -getting distributed to the CPU or even the hard drive, which may ultimately -result in extremely slow queries. - -```shell -python -m pip install "accelerate>=0.16.0,<1.0" -``` - -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.StableLM.v1" -> name = "stablelm-tuned-alpha-7b" -> ``` - -| Argument | Description | -| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | The name of a StableLM model that is supported (e. g. "stablelm-tuned-alpha-7b"). ~~Literal["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]~~ | -| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ | -| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | - -See the -[Stability AI StableLM GitHub repo](https://github.com/Stability-AI/StableLM/#stablelm-alpha) -for details. - -Note that Hugging Face will download this model the first time you use it - you +Note that Hugging Face will download the model the first time you use it - you can [define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) by setting the environmental variable `HF_HOME`. -#### spacy.OpenLLaMA.v1 {id="openllama"} +#### Installation with HuggingFace {id="install-hf"} -To use this model, ideally you have a GPU enabled and have installed - -- `transformers[sentencepiece]` -- `torch` -- CUDA in your virtual environment. +To use models from HuggingFace, ideally you have a GPU enabled and have +installed `transformers`, `torch` and CUDA in your virtual environment. This +allows you to have the setting `device=cuda:0` in your config, which ensures +that the model is loaded entirely on the GPU (and fails otherwise). You can do so with @@ -1340,30 +1073,7 @@ result in extremely slow queries. python -m pip install "accelerate>=0.16.0,<1.0" ``` -> #### Example config -> -> ```ini -> [components.llm.model] -> @llm_models = "spacy.OpenLLaMA.v1" -> name = "open_llama_3b" -> ``` - -| Argument | Description | -| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `name` | The name of a OpenLLaMA model that is supported. ~~Literal["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]~~ | -| `config_init` | Further configuration passed on to the construction of the model with `transformers.AutoModelForCausalLM.from_pretrained()`. Defaults to `{}`. ~~Dict[str, Any]~~ | -| `config_run` | Further configuration used during model inference. Defaults to `{}`. ~~Dict[str, Any]~~ | - -See the -[OpenLM Research OpenLLaMA GitHub repo](https://github.com/openlm-research/open_llama) -for details. - -Note that Hugging Face will download this model the first time you use it - you -can -[define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) -by setting the environmental variable `HF_HOME`. - -#### LangChain models {id="langchain-models"} +### LangChain models {id="langchain-models"} To use [LangChain](https://github.com/hwchase17/langchain) for the API retrieval part, make sure you have installed it first: @@ -1392,7 +1102,7 @@ The name of the model to be used has to be passed in via the `name` attribute. > @llm_models = "langchain.OpenAI.v1" > name = "gpt-3.5-turbo" > query = {"@llm_queries": "spacy.CallLangChain.v1"} -> config = {"temperature": 0.3} +> config = {"temperature": 0.0} > ``` | Argument | Description | @@ -1404,7 +1114,7 @@ The name of the model to be used has to be passed in via the `name` attribute. The default `query` (`spacy.CallLangChain.v1`) executes the prompts by running `model(text)` for each given textual prompt. -### Cache {id="cache"} +## Cache {id="cache"} Interacting with LLMs, either through an external API or a local instance, is costly. Since developing an NLP pipeline generally means a lot of exploration @@ -1436,9 +1146,9 @@ provide your own registered function returning your own cache implementation. If you wish to do so, ensure that your cache object adheres to the `Protocol` defined in `spacy_llm.ty.Cache`. -### Various functions {id="various-functions"} +## Various functions {id="various-functions"} -#### spacy.FewShotReader.v1 {id="fewshotreader-v1"} +### spacy.FewShotReader.v1 {id="fewshotreader-v1"} This function is registered in spaCy's `misc` registry, and reads in examples from a `.yml`, `.yaml`, `.json` or `.jsonl` file. It uses @@ -1457,7 +1167,7 @@ them depending on the file extension. | -------- | ----------------------------------------------------------------------------------------------- | | `path` | Path to an examples file with suffix `.yml`, `.yaml`, `.json` or `.jsonl`. ~~Union[str, Path]~~ | -#### spacy.FileReader.v1 {id="filereader-v1"} +### spacy.FileReader.v1 {id="filereader-v1"} This function is registered in spaCy's `misc` registry, and reads a file provided to the `path` to return a `str` representation of its contents. This @@ -1477,7 +1187,7 @@ template. | -------- | ------------------------------------------------- | | `path` | Path to the file to be read. ~~Union[str, Path]~~ | -#### Normalizer functions {id="normalizer-functions"} +### Normalizer functions {id="normalizer-functions"} These functions provide simple normalizations for string comparisons, e.g. between a list of specified labels and a label given in the raw text of the LLM diff --git a/website/docs/usage/large-language-models.mdx b/website/docs/usage/large-language-models.mdx index 4da9a8f16..86f44f5ae 100644 --- a/website/docs/usage/large-language-models.mdx +++ b/website/docs/usage/large-language-models.mdx @@ -108,7 +108,7 @@ labels = ["COMPLIMENT", "INSULT"] [components.llm.model] @llm_models = "spacy.GPT-3-5.v1" -config = {"temperature": 0.3} +config = {"temperature": 0.0} ``` Now run: @@ -142,7 +142,7 @@ pipeline = ["llm"] factory = "llm" [components.llm.task] -@llm_tasks = "spacy.NER.v2" +@llm_tasks = "spacy.NER.v3" labels = ["PERSON", "ORGANISATION", "LOCATION"] [components.llm.model] @@ -169,25 +169,17 @@ to be `"databricks/dolly-v2-12b"` for better performance. ### Example 3: Create the component directly in Python {id="example-3"} -The `llm` component behaves as any other component does, so adding it to an -existing pipeline follows the same pattern: +The `llm` component behaves as any other component does, and there are +[task-specific components](/api/large-language-models#config) defined to +help you hit the ground running with a reasonable built-in task implementation. ```python import spacy nlp = spacy.blank("en") -nlp.add_pipe( - "llm", - config={ - "task": { - "@llm_tasks": "spacy.NER.v2", - "labels": ["PERSON", "ORGANISATION", "LOCATION"] - }, - "model": { - "@llm_models": "spacy.GPT-3-5.v1", - }, - }, -) +llm_ner = nlp.add_pipe("llm_ner") +llm_ner.add_label("PERSON") +llm_ner.add_label("LOCATION") nlp.initialize() doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes") print([(ent.text, ent.label_) for ent in doc.ents]) @@ -314,7 +306,7 @@ COMPLIMENT ## API {id="api"} -`spacy-llm` exposes a `llm` factory with +`spacy-llm` exposes an `llm` factory with [configurable settings](/api/large-language-models#config). An `llm` component is defined by two main settings: @@ -359,24 +351,26 @@ function. | [`task.parse_responses`](/api/large-language-models#task-parse-responses) | Takes a collection of LLM responses and the original documents, parses the responses into structured information, and sets the annotations on the documents. | Moreover, the task may define an optional [`scorer` method](/api/scorer#score). -It should accept an iterable of `Example`s as input and return a score +It should accept an iterable of `Example` objects as input and return a score dictionary. If the `scorer` method is defined, `spacy-llm` will call it to evaluate the component. -| Component | Description | -| ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. | -| [`spacy.NER.v2`](/api/large-language-models#ner-v2) | The built-in NER task supports both zero-shot and few-shot prompting. This version also supports explicitly defining the provided labels with custom descriptions. | -| [`spacy.NER.v1`](/api/large-language-models#ner-v1) | The original version of the built-in NER task supports both zero-shot and few-shot prompting. | -| [`spacy.SpanCat.v2`](/api/large-language-models#spancat-v2) | The built-in SpanCat task is a simple adaptation of the NER task to support overlapping entities and store its annotations in `doc.spans`. | -| [`spacy.SpanCat.v1`](/api/large-language-models#spancat-v1) | The original version of the built-in SpanCat task is a simple adaptation of the v1 NER task to support overlapping entities and store its annotations in `doc.spans`. | -| [`spacy.TextCat.v3`](/api/large-language-models#textcat-v3) | Version 3 (the most recent) of the built-in TextCat task supports both zero-shot and few-shot prompting. It allows setting definitions of labels. | -| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 of the built-in TextCat task supports both zero-shot and few-shot prompting and includes an improved prompt template. | -| [`spacy.TextCat.v1`](/api/large-language-models#textcat-v1) | Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting. | -| [`spacy.REL.v1`](/api/large-language-models#rel-v1) | The built-in REL task supports both zero-shot and few-shot prompting. It relies on an upstream NER component for entities extraction. | -| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | The `Lemma.v1` task lemmatizes the provided text and updates the `lemma_` attribute in the doc's tokens accordingly. | -| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. | -| [`spacy.NoOp.v1`](/api/large-language-models#noop-v1) | This task is only useful for testing - it tells the LLM to do nothing, and does not set any fields on the `docs`. | +| Component | Description | +| ----------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | +| [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text. | +| [`spacy.NER.v3`](/api/large-language-models#ner-v3) | Implements Chain-of-Thought reasoning for NER extraction - obtains higher accuracy than v1 or v2. | +| [`spacy.NER.v2`](/api/large-language-models#ner-v2) | Builds on v1 and additionally supports defining the provided labels with explicit descriptions. | +| [`spacy.NER.v1`](/api/large-language-models#ner-v1) | The original version of the built-in NER task supports both zero-shot and few-shot prompting. | +| [`spacy.SpanCat.v3`](/api/large-language-models#spancat-v3) | Adaptation of the v3 NER task to support overlapping entities and store its annotations in `doc.spans`. | +| [`spacy.SpanCat.v2`](/api/large-language-models#spancat-v2) | Adaptation of the v2 NER task to support overlapping entities and store its annotations in `doc.spans`. | +| [`spacy.SpanCat.v1`](/api/large-language-models#spancat-v1) | Adaptation of the v1 NER task to support overlapping entities and store its annotations in `doc.spans`. | +| [`spacy.REL.v1`](/api/large-language-models#rel-v1) | Relation Extraction task supporting both zero-shot and few-shot prompting. | +| [`spacy.TextCat.v3`](/api/large-language-models#textcat-v3) | Version 3 builds on v2 and allows setting definitions of labels. | +| [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2) | Version 2 builds on v1 and includes an improved prompt template. | +| [`spacy.TextCat.v1`](/api/large-language-models#textcat-v1) | Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting. | +| [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1) | Lemmatizes the provided text and updates the `lemma_` attribute of the tokens accordingly. | +| [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1) | Performs sentiment analysis on provided texts. | +| [`spacy.NoOp.v1`](/api/large-language-models#noop-v1) | This task is only useful for testing - it tells the LLM to do nothing, and does not set any fields on the `docs`. | #### Providing examples for few-shot prompts {id="few-shot-prompts"} @@ -469,31 +463,38 @@ provider's documentation. -| Component | Description | -| ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------ | -| [`spacy.GPT-4.v1`](/api/large-language-models#gpt-4) | OpenAI’s `gpt-4` model family. | -| [`spacy.GPT-3-5.v1`](/api/large-language-models#gpt-3-5) | OpenAI’s `gpt-3-5` model family. | -| [`spacy.Text-Davinci.v1`](/api/large-language-models#text-davinci) | OpenAI’s `text-davinci` model family. | -| [`spacy.Code-Davinci.v1`](/api/large-language-models#code-davinci) | OpenAI’s `code-davinci` model family. | -| [`spacy.Text-Curie.v1`](/api/large-language-models#text-curie) | OpenAI’s `text-curie` model family. | -| [`spacy.Text-Babbage.v1`](/api/large-language-models#text-babbage) | OpenAI’s `text-babbage` model family. | -| [`spacy.Text-Ada.v1`](/api/large-language-models#text-ada) | OpenAI’s `text-ada` model family. | -| [`spacy.Davinci.v1`](/api/large-language-models#davinci) | OpenAI’s `davinci` model family. | -| [`spacy.Curie.v1`](/api/large-language-models#curie) | OpenAI’s `curie` model family. | -| [`spacy.Babbage.v1`](/api/large-language-models#babbage) | OpenAI’s `babbage` model family. | -| [`spacy.Ada.v1`](/api/large-language-models#ada) | OpenAI’s `ada` model family. | -| [`spacy.Command.v1`](/api/large-language-models#command) | Cohere’s `command` model family. | -| [`spacy.Claude-1.v1`](/api/large-language-models#claude-1) | Anthropic’s `claude-1` model family. | -| [`spacy.Claude-instant-1.v1`](/api/large-language-models#claude-instant-1) | Anthropic’s `claude-instant-1` model family. | -| [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#claude-instant-1-1) | Anthropic’s `claude-instant-1.1` model family. | -| [`spacy.Claude-1-0.v1`](/api/large-language-models#claude-1-0) | Anthropic’s `claude-1.0` model family. | -| [`spacy.Claude-1-2.v1`](/api/large-language-models#claude-1-2) | Anthropic’s `claude-1.2` model family. | -| [`spacy.Claude-1-3.v1`](/api/large-language-models#claude-1-3) | Anthropic’s `claude-1.3` model family. | -| [`spacy.Dolly.v1`](/api/large-language-models#dolly) | Dolly models through [Databricks](https://huggingface.co/databricks) on HuggingFace. | -| [`spacy.Falcon.v1`](/api/large-language-models#falcon) | Falcon model through HuggingFace. | -| [`spacy.StableLM.v1`](/api/large-language-models#stablelm) | StableLM model through HuggingFace. | -| [`spacy.OpenLLaMA.v1`](/api/large-language-models#openllama) | OpenLLaMA model through HuggingFace. | -| [LangChain models](/api/large-language-models#langchain-models) | LangChain models for API retrieval. | +| Model | Description | +| ----------------------------------------------------------------------- | ---------------------------------------------- | +| [`spacy.GPT-4.v2`](/api/large-language-models#models-rest) | OpenAI’s `gpt-4` model family. | +| [`spacy.GPT-3-5.v2`](/api/large-language-models#models-rest) | OpenAI’s `gpt-3-5` model family. | +| [`spacy.Text-Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-davinci` model family. | +| [`spacy.Code-Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `code-davinci` model family. | +| [`spacy.Text-Curie.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-curie` model family. | +| [`spacy.Text-Babbage.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-babbage` model family. | +| [`spacy.Text-Ada.v2`](/api/large-language-models#models-rest) | OpenAI’s `text-ada` model family. | +| [`spacy.Davinci.v2`](/api/large-language-models#models-rest) | OpenAI’s `davinci` model family. | +| [`spacy.Curie.v2`](/api/large-language-models#models-rest) | OpenAI’s `curie` model family. | +| [`spacy.Babbage.v2`](/api/large-language-models#models-rest) | OpenAI’s `babbage` model family. | +| [`spacy.Ada.v2`](/api/large-language-models#models-rest) | OpenAI’s `ada` model family. | +| [`spacy.Command.v1`](/api/large-language-models#models-rest) | Cohere’s `command` model family. | +| [`spacy.Claude-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-2` model family. | +| [`spacy.Claude-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1` model family. | +| [`spacy.Claude-instant-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1` model family. | +| [`spacy.Claude-instant-1-1.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-instant-1.1` model family. | +| [`spacy.Claude-1-0.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.0` model family. | +| [`spacy.Claude-1-2.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.2` model family. | +| [`spacy.Claude-1-3.v1`](/api/large-language-models#models-rest) | Anthropic’s `claude-1.3` model family. | +| [`spacy.Dolly.v1`](/api/large-language-models#models-hf) | Dolly models through HuggingFace. | +| [`spacy.Falcon.v1`](/api/large-language-models#models-hf) | Falcon models through HuggingFace. | +| [`spacy.Llama2.v1`](/api/large-language-models#models-hf) | Llama2 models through HuggingFace. | +| [`spacy.StableLM.v1`](/api/large-language-models#models-hf) | StableLM models through HuggingFace. | +| [`spacy.OpenLLaMA.v1`](/api/large-language-models#models-hf) | OpenLLaMA models through HuggingFace. | +| [LangChain models](/api/large-language-models#langchain-models) | LangChain models for API retrieval. | + +Note that the chat models variants of Llama 2 are currently not supported. This +is because they need a particular prompting setup and don't add any discernible +benefits in the use case of `spacy-llm` (i. e. no interactive chat) compared to +the completion model variants. ### Cache {id="cache"} @@ -505,7 +506,7 @@ documents at each run that keeps batches of documents stored on disk. ### Various functions {id="various-functions"} -| Component | Description | +| Function | Description | | ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | [`spacy.FewShotReader.v1`](/api/large-language-models#fewshotreader-v1) | This function is registered in spaCy's `misc` registry, and reads in examples from a `.yml`, `.yaml`, `.json` or `.jsonl` file. It uses [`srsly`](https://github.com/explosion/srsly) to read in these files and parses them depending on the file extension. | | [`spacy.FileReader.v1`](/api/large-language-models#filereader-v1) | This function is registered in spaCy's `misc` registry, and reads a file provided to the `path` to return a `str` representation of its contents. This function is typically used to read [Jinja](https://jinja.palletsprojects.com/en/3.1.x/) files containing the prompt template. | From 013762be4150d29ceb25d4339e8a934c32fcf195 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Fri, 8 Sep 2023 11:35:38 +0200 Subject: [PATCH 54/74] Few spacy-llm doc fixes (#12969) * fix construction example * shorten task-specific factory list * small edits to HF models * small edit to API models * typo * fix space Co-authored-by: Raphael Mitsch --------- Co-authored-by: Raphael Mitsch --- website/docs/api/large-language-models.mdx | 24 +++++++++------------- 1 file changed, 10 insertions(+), 14 deletions(-) diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx index 1ac9b0cef..d32368e22 100644 --- a/website/docs/api/large-language-models.mdx +++ b/website/docs/api/large-language-models.mdx @@ -19,26 +19,20 @@ prototyping** and **prompting**, and turning unstructured responses into An LLM component is implemented through the `LLMWrapper` class. It is accessible through a generic `llm` [component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories) -as well as through task-specific component factories: - -- `llm_ner` -- `llm_spancat` -- `llm_rel` -- `llm_textcat` -- `llm_sentiment` -- `llm_summarization` +as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`, +`llm_textcat`, `llm_sentiment` and `llm_summarization`. ### LLMWrapper.\_\_init\_\_ {id="init",tag="method"} > #### Example > > ```python -> # Construction via add_pipe with default GPT3.5 model and NER task +> # Construction via add_pipe with the default GPT 3.5 model and an explicitly defined task > config = {"task": {"@llm_tasks": "spacy.NER.v3", "labels": ["PERSON", "ORGANISATION", "LOCATION"]}} -> llm = nlp.add_pipe("llm") +> llm = nlp.add_pipe("llm", config=config) > -> # Construction via add_pipe with task-specific factory and default GPT3.5 model -> parser = nlp.add_pipe("llm-ner", config=config) +> # Construction via add_pipe with a task-specific factory and default GPT3.5 model +> llm = nlp.add_pipe("llm-ner") > > # Construction from class > from spacy_llm.pipeline import LLMWrapper @@ -956,6 +950,8 @@ provider's API. > config = {"temperature": 0.0} > ``` +Currently, these models are provided as part of the core library: + | Model | Provider | Supported names | Default name | Default config | | ----------------------------- | --------- | ---------------------------------------------------------------------------------------- | ---------------------- | ------------------------------------ | | `spacy.GPT-4.v1` | OpenAI | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]` | `"gpt-4"` | `{}` | @@ -1036,6 +1032,8 @@ These models all take the same parameters: > name = "llama2-7b-hf" > ``` +Currently, these models are provided as part of the core library: + | Model | Provider | Supported names | HF directory | | -------------------- | --------------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------- | | `spacy.Dolly.v1` | Databricks | `["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]` | https://huggingface.co/databricks | @@ -1044,8 +1042,6 @@ These models all take the same parameters: | `spacy.StableLM.v1` | Stability AI | `["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]` | https://huggingface.co/stabilityai | | `spacy.OpenLLaMA.v1` | OpenLM Research | `["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]` | https://huggingface.co/openlm-research | -See the "HF directory" for more details on each of the models. - Note that Hugging Face will download the model the first time you use it - you can [define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache) From 36d4767aca313ff436d398787d8df09b58678b50 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Wed, 13 Sep 2023 13:16:05 +0200 Subject: [PATCH 55/74] Skip project remotes test for python 3.12 (#12980) `weasel` (using `cloudpathlib`) does not currently support remote paths for python 3.12. --- spacy/tests/test_cli_app.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/spacy/tests/test_cli_app.py b/spacy/tests/test_cli_app.py index 3a426113b..a2fd4d666 100644 --- a/spacy/tests/test_cli_app.py +++ b/spacy/tests/test_cli_app.py @@ -1,4 +1,5 @@ import os +import sys from pathlib import Path import pytest @@ -213,6 +214,9 @@ def test_project_clone(options): assert (out / "README.md").is_file() +@pytest.mark.skipif( + sys.version_info >= (3, 12), reason="Python 3.12+ not supported for remotes" +) def test_project_push_pull(project_dir): proj = dict(SAMPLE_PROJECT) remote = "xyz" From 8f0d6b0a8c42e4852bf6e24cdf629043f2f39361 Mon Sep 17 00:00:00 2001 From: Sofie Van Landeghem Date: Wed, 13 Sep 2023 13:21:58 +0200 Subject: [PATCH 56/74] Fix in BertTokenizer docs (#12955) * fix BertWordPieceTokenizer constructor call * fix * Update website/docs/usage/linguistic-features.mdx --------- Co-authored-by: Adriane Boyd --- website/docs/usage/linguistic-features.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/docs/usage/linguistic-features.mdx b/website/docs/usage/linguistic-features.mdx index 90f305ada..a58e8a241 100644 --- a/website/docs/usage/linguistic-features.mdx +++ b/website/docs/usage/linguistic-features.mdx @@ -1299,9 +1299,9 @@ correct type. ```python {title="functions.py",highlight="1"} @spacy.registry.tokenizers("bert_word_piece_tokenizer") -def create_whitespace_tokenizer(vocab_file: str, lowercase: bool): +def create_bert_tokenizer(vocab_file: str, lowercase: bool): def create_tokenizer(nlp): - return BertWordPieceTokenizer(nlp.vocab, vocab_file, lowercase) + return BertTokenizer(nlp.vocab, vocab_file, lowercase) return create_tokenizer ``` From 4e3360ad12c924b37185c238e832c10ac3ad9e15 Mon Sep 17 00:00:00 2001 From: Eliana Vornov Date: Mon, 25 Sep 2023 05:25:41 -0400 Subject: [PATCH 57/74] add --spans-key option for CLI spancat evaluation (#12981) * add span key option for CLI evaluation * Rephrase CLI help to refer to Doc.spans instead of spancat * Rephrase docs to refer to Doc.spans instead of spancat --------- Co-authored-by: Adriane Boyd --- spacy/cli/evaluate.py | 2 ++ website/docs/api/cli.mdx | 3 ++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py index 6235b658d..2276ca6b0 100644 --- a/spacy/cli/evaluate.py +++ b/spacy/cli/evaluate.py @@ -28,6 +28,7 @@ def evaluate_cli( displacy_path: Optional[Path] = Opt(None, "--displacy-path", "-dp", help="Directory to output rendered parses as HTML", exists=True, file_okay=False), displacy_limit: int = Opt(25, "--displacy-limit", "-dl", help="Limit of parses to render as HTML"), per_component: bool = Opt(False, "--per-component", "-P", help="Return scores per component, only applicable when an output JSON file is specified."), + spans_key: str = Opt("sc", "--spans-key", "-sk", help="Spans key to use when evaluating Doc.spans"), # fmt: on ): """ @@ -53,6 +54,7 @@ def evaluate_cli( displacy_limit=displacy_limit, per_component=per_component, silent=False, + spans_key=spans_key, ) diff --git a/website/docs/api/cli.mdx b/website/docs/api/cli.mdx index d63ac6e1d..2646a848b 100644 --- a/website/docs/api/cli.mdx +++ b/website/docs/api/cli.mdx @@ -1183,7 +1183,7 @@ skew. To render a sample of dependency parses in a HTML file using the `--displacy-path` argument. ```bash -$ python -m spacy benchmark accuracy [model] [data_path] [--output] [--code] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit] +$ python -m spacy benchmark accuracy [model] [data_path] [--output] [--code] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit] [--per-component] [--spans-key] ``` | Name | Description | @@ -1197,6 +1197,7 @@ $ python -m spacy benchmark accuracy [model] [data_path] [--output] [--code] [-- | `--displacy-path`, `-dp` | Directory to output rendered parses as HTML. If not set, no visualizations will be generated. ~~Optional[Path] \(option)~~ | | `--displacy-limit`, `-dl` | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. ~~int (option)~~ | | `--per-component`, `-P` 3.6 | Whether to return the scores keyed by component name. Defaults to `False`. ~~bool (flag)~~ | +| `--spans-key`, `-sk` | Spans key to use when evaluating `Doc.spans`. Defaults to `sc`. ~~str (option)~~ | | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | | **CREATES** | Training results and optional metrics and visualizations. | From ed8c11e2aac43d0a378377823188e30c367391e4 Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Mon, 25 Sep 2023 18:44:35 +0900 Subject: [PATCH 58/74] Fix typo in lemmatizer.py (#13003) specfic -> specific --- spacy/lang/es/lemmatizer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spacy/lang/es/lemmatizer.py b/spacy/lang/es/lemmatizer.py index 44f968347..ee5d38e84 100644 --- a/spacy/lang/es/lemmatizer.py +++ b/spacy/lang/es/lemmatizer.py @@ -163,7 +163,7 @@ class SpanishLemmatizer(Lemmatizer): for old, new in self.lookups.get_table("lemma_rules").get("det", []): if word == old: return [new] - # If none of the specfic rules apply, search in the common rules for + # If none of the specific rules apply, search in the common rules for # determiners and pronouns that follow a unique pattern for # lemmatization. If the word is in the list, return the corresponding # lemma. @@ -291,7 +291,7 @@ class SpanishLemmatizer(Lemmatizer): for old, new in self.lookups.get_table("lemma_rules").get("pron", []): if word == old: return [new] - # If none of the specfic rules apply, search in the common rules for + # If none of the specific rules apply, search in the common rules for # determiners and pronouns that follow a unique pattern for # lemmatization. If the word is in the list, return the corresponding # lemma. From 935a5455b696635119dc879205c92d442da67cb6 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Mon, 25 Sep 2023 11:49:28 +0200 Subject: [PATCH 59/74] Docs: add new tag for evaluate CLI --spans-keys (#13013) --- website/docs/api/cli.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/api/cli.mdx b/website/docs/api/cli.mdx index 2646a848b..e6b04a930 100644 --- a/website/docs/api/cli.mdx +++ b/website/docs/api/cli.mdx @@ -1197,7 +1197,7 @@ $ python -m spacy benchmark accuracy [model] [data_path] [--output] [--code] [-- | `--displacy-path`, `-dp` | Directory to output rendered parses as HTML. If not set, no visualizations will be generated. ~~Optional[Path] \(option)~~ | | `--displacy-limit`, `-dl` | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. ~~int (option)~~ | | `--per-component`, `-P` 3.6 | Whether to return the scores keyed by component name. Defaults to `False`. ~~bool (flag)~~ | -| `--spans-key`, `-sk` | Spans key to use when evaluating `Doc.spans`. Defaults to `sc`. ~~str (option)~~ | +| `--spans-key`, `-sk` 3.6.2 | Spans key to use when evaluating `Doc.spans`. Defaults to `sc`. ~~str (option)~~ | | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | | **CREATES** | Training results and optional metrics and visualizations. | From ff4215f1c7c99c1728a5a77c8283b91f71804cd4 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Mon, 25 Sep 2023 14:48:38 +0200 Subject: [PATCH 60/74] Drop support for python 3.6 (#13009) * Drop support for python 3.6 * Update docs --- .github/workflows/tests.yml | 3 --- README.md | 2 +- build-constraints.txt | 6 +++--- requirements.txt | 5 ++--- setup.cfg | 9 +++------ website/docs/usage/index.mdx | 2 +- 6 files changed, 10 insertions(+), 17 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 2f74d887d..f68280be2 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -60,8 +60,6 @@ jobs: os: [ubuntu-latest, windows-latest, macos-latest] python_version: ["3.11"] include: - - os: ubuntu-20.04 - python_version: "3.6" - os: windows-latest python_version: "3.7" - os: macos-latest @@ -95,7 +93,6 @@ jobs: - name: Run mypy run: | python -m mypy spacy - if: matrix.python_version != '3.6' - name: Delete source directory and .egg-info run: | diff --git a/README.md b/README.md index 59d3ee9ee..02c2e1baf 100644 --- a/README.md +++ b/README.md @@ -109,7 +109,7 @@ For detailed installation instructions, see the - **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual Studio) -- **Python version**: Python 3.6+ (only 64 bit) +- **Python version**: Python 3.7+ (only 64 bit) - **Package managers**: [pip] · [conda] (via `conda-forge`) [pip]: https://pypi.org/project/spacy/ diff --git a/build-constraints.txt b/build-constraints.txt index 5540d634d..b1cf596ca 100644 --- a/build-constraints.txt +++ b/build-constraints.txt @@ -1,6 +1,6 @@ -# build version constraints for use with wheelwright + multibuild -numpy==1.15.0; python_version<='3.7' and platform_machine!='aarch64' -numpy==1.19.2; python_version<='3.7' and platform_machine=='aarch64' +# build version constraints for use with wheelwright +numpy==1.15.0; python_version=='3.7' and platform_machine!='aarch64' +numpy==1.19.2; python_version=='3.7' and platform_machine=='aarch64' numpy==1.17.3; python_version=='3.8' and platform_machine!='aarch64' numpy==1.19.2; python_version=='3.8' and platform_machine=='aarch64' numpy>=1.25.0; python_version>='3.9' diff --git a/requirements.txt b/requirements.txt index b6cc542a5..f711d0012 100644 --- a/requirements.txt +++ b/requirements.txt @@ -33,12 +33,11 @@ pytest-timeout>=1.3.0,<2.0.0 mock>=2.0.0,<3.0.0 flake8>=3.8.0,<6.0.0 hypothesis>=3.27.0,<7.0.0 -mypy>=0.990,<1.1.0; platform_machine != "aarch64" and python_version >= "3.7" -types-dataclasses>=0.1.3; python_version < "3.7" +mypy>=0.990,<1.1.0; platform_machine != "aarch64" types-mock>=0.1.1 types-setuptools>=57.0.0 types-requests types-setuptools>=57.0.0 black==22.3.0 -cython-lint>=0.15.0; python_version >= "3.7" +cython-lint>=0.15.0 isort>=5.0,<6.0 diff --git a/setup.cfg b/setup.cfg index 9a5388c80..a6b60ba59 100644 --- a/setup.cfg +++ b/setup.cfg @@ -17,7 +17,6 @@ classifiers = Operating System :: Microsoft :: Windows Programming Language :: Cython Programming Language :: Python :: 3 - Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 @@ -31,15 +30,13 @@ project_urls = [options] zip_safe = false include_package_data = true -python_requires = >=3.6 +python_requires = >=3.7 # NOTE: This section is superseded by pyproject.toml and will be removed in # spaCy v4 setup_requires = cython>=0.25,<3.0 - # The newest supported pip for python 3.6 has bugs related to markers in - # this section, so this does not contain the same constraints as - # pyproject.toml - numpy>=1.15.0 + numpy>=1.15.0; python_version < "3.9" + numpy>=1.19.0; python_version >= "3.9" # We also need our Cython packages here to compile against cymem>=2.0.2,<2.1.0 preshed>=3.0.2,<3.1.0 diff --git a/website/docs/usage/index.mdx b/website/docs/usage/index.mdx index 414968d42..c50e9db6c 100644 --- a/website/docs/usage/index.mdx +++ b/website/docs/usage/index.mdx @@ -20,7 +20,7 @@ menu: ## Installation instructions {id="installation"} -spaCy is compatible with **64-bit CPython 3.6+** and runs on **Unix/Linux**, +spaCy is compatible with **64-bit CPython 3.7+** and runs on **Unix/Linux**, **macOS/OS X** and **Windows**. The latest spaCy releases are available over [pip](https://pypi.python.org/pypi/spacy) and [conda](https://anaconda.org/conda-forge/spacy). From b4501db6f8bff096f2a2103f998e81cd2dd1dfa2 Mon Sep 17 00:00:00 2001 From: Madeesh Kannan Date: Mon, 25 Sep 2023 18:20:30 +0200 Subject: [PATCH 61/74] Update emoji library in rule-based matcher example (#13014) --- website/docs/usage/rule-based-matching.mdx | 58 +++++++++++----------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/website/docs/usage/rule-based-matching.mdx b/website/docs/usage/rule-based-matching.mdx index 4f54415cb..d01107ea2 100644 --- a/website/docs/usage/rule-based-matching.mdx +++ b/website/docs/usage/rule-based-matching.mdx @@ -850,14 +850,14 @@ negative pattern. To keep it simple, we'll either add or subtract `0.1` points this way, the score will also reflect combinations of emoji, even positive _and_ negative ones. -With a library like [Emojipedia](https://github.com/bcongdon/python-emojipedia), -we can also retrieve a short description for each emoji – for example, 😍's -official title is "Smiling Face With Heart-Eyes". Assigning it to a +With a library like [emoji](https://github.com/carpedm20/emoji), we can also +retrieve a short description for each emoji – for example, 😍's official title +is "Smiling Face With Heart-Eyes". Assigning it to a [custom attribute](/usage/processing-pipelines#custom-components-attributes) on the emoji span will make it available as `span._.emoji_desc`. ```python -from emojipedia import Emojipedia # Installation: pip install emojipedia +import emoji # Installation: pip install emoji from spacy.tokens import Span # Get the global Span object Span.set_extension("emoji_desc", default=None) # Register the custom attribute @@ -869,9 +869,9 @@ def label_sentiment(matcher, doc, i, matches): elif doc.vocab.strings[match_id] == "SAD": doc.sentiment -= 0.1 # Subtract 0.1 for negative sentiment span = doc[start:end] - emoji = Emojipedia.search(span[0].text) # Get data for emoji - span._.emoji_desc = emoji.title # Assign emoji description - + # Verify if it is an emoji and set the extension attribute correctly. + if emoji.is_emoji(span[0].text): + span._.emoji_desc = emoji.demojize(span[0].text, delimiters=("", ""), language=doc.lang_).replace("_", " ") ``` To label the hashtags, we can use a @@ -1096,28 +1096,28 @@ The following operators are supported by the `DependencyMatcher`, most of which come directly from [Semgrex](https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/semgraph/semgrex/SemgrexPattern.html): -| Symbol | Description | -| --------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | -| `A < B` | `A` is the immediate dependent of `B`. | -| `A > B` | `A` is the immediate head of `B`. | -| `A << B` | `A` is the dependent in a chain to `B` following dep → head paths. | -| `A >> B` | `A` is the head in a chain to `B` following head → dep paths. | -| `A . B` | `A` immediately precedes `B`, i.e. `A.i == B.i - 1`, and both are within the same dependency tree. | -| `A .* B` | `A` precedes `B`, i.e. `A.i < B.i`, and both are within the same dependency tree _(Semgrex counterpart: `..`)_. | -| `A ; B` | `A` immediately follows `B`, i.e. `A.i == B.i + 1`, and both are within the same dependency tree _(Semgrex counterpart: `-`)_. | -| `A ;* B` | `A` follows `B`, i.e. `A.i > B.i`, and both are within the same dependency tree _(Semgrex counterpart: `--`)_. | -| `A $+ B` | `B` is a right immediate sibling of `A`, i.e. `A` and `B` have the same parent and `A.i == B.i - 1`. | -| `A $- B` | `B` is a left immediate sibling of `A`, i.e. `A` and `B` have the same parent and `A.i == B.i + 1`. | -| `A $++ B` | `B` is a right sibling of `A`, i.e. `A` and `B` have the same parent and `A.i < B.i`. | -| `A $-- B` | `B` is a left sibling of `A`, i.e. `A` and `B` have the same parent and `A.i > B.i`. | -| `A >+ B` 3.5.1 | `B` is a right immediate child of `A`, i.e. `A` is a parent of `B` and `A.i == B.i - 1` _(not in Semgrex)_. | -| `A >- B` 3.5.1 | `B` is a left immediate child of `A`, i.e. `A` is a parent of `B` and `A.i == B.i + 1` _(not in Semgrex)_. | -| `A >++ B` | `B` is a right child of `A`, i.e. `A` is a parent of `B` and `A.i < B.i`. | -| `A >-- B` | `B` is a left child of `A`, i.e. `A` is a parent of `B` and `A.i > B.i`. | -| `A <+ B` 3.5.1 | `B` is a right immediate parent of `A`, i.e. `A` is a child of `B` and `A.i == B.i - 1` _(not in Semgrex)_. | -| `A <- B` 3.5.1 | `B` is a left immediate parent of `A`, i.e. `A` is a child of `B` and `A.i == B.i + 1` _(not in Semgrex)_. | -| `A <++ B` | `B` is a right parent of `A`, i.e. `A` is a child of `B` and `A.i < B.i`. | -| `A <-- B` | `B` is a left parent of `A`, i.e. `A` is a child of `B` and `A.i > B.i`. | +| Symbol | Description | +| --------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | +| `A < B` | `A` is the immediate dependent of `B`. | +| `A > B` | `A` is the immediate head of `B`. | +| `A << B` | `A` is the dependent in a chain to `B` following dep → head paths. | +| `A >> B` | `A` is the head in a chain to `B` following head → dep paths. | +| `A . B` | `A` immediately precedes `B`, i.e. `A.i == B.i - 1`, and both are within the same dependency tree. | +| `A .* B` | `A` precedes `B`, i.e. `A.i < B.i`, and both are within the same dependency tree _(Semgrex counterpart: `..`)_. | +| `A ; B` | `A` immediately follows `B`, i.e. `A.i == B.i + 1`, and both are within the same dependency tree _(Semgrex counterpart: `-`)_. | +| `A ;* B` | `A` follows `B`, i.e. `A.i > B.i`, and both are within the same dependency tree _(Semgrex counterpart: `--`)_. | +| `A $+ B` | `B` is a right immediate sibling of `A`, i.e. `A` and `B` have the same parent and `A.i == B.i - 1`. | +| `A $- B` | `B` is a left immediate sibling of `A`, i.e. `A` and `B` have the same parent and `A.i == B.i + 1`. | +| `A $++ B` | `B` is a right sibling of `A`, i.e. `A` and `B` have the same parent and `A.i < B.i`. | +| `A $-- B` | `B` is a left sibling of `A`, i.e. `A` and `B` have the same parent and `A.i > B.i`. | +| `A >+ B` 3.5.1 | `B` is a right immediate child of `A`, i.e. `A` is a parent of `B` and `A.i == B.i - 1` _(not in Semgrex)_. | +| `A >- B` 3.5.1 | `B` is a left immediate child of `A`, i.e. `A` is a parent of `B` and `A.i == B.i + 1` _(not in Semgrex)_. | +| `A >++ B` | `B` is a right child of `A`, i.e. `A` is a parent of `B` and `A.i < B.i`. | +| `A >-- B` | `B` is a left child of `A`, i.e. `A` is a parent of `B` and `A.i > B.i`. | +| `A <+ B` 3.5.1 | `B` is a right immediate parent of `A`, i.e. `A` is a child of `B` and `A.i == B.i - 1` _(not in Semgrex)_. | +| `A <- B` 3.5.1 | `B` is a left immediate parent of `A`, i.e. `A` is a child of `B` and `A.i == B.i + 1` _(not in Semgrex)_. | +| `A <++ B` | `B` is a right parent of `A`, i.e. `A` is a child of `B` and `A.i < B.i`. | +| `A <-- B` | `B` is a left parent of `A`, i.e. `A` is a child of `B` and `A.i > B.i`. | ### Designing dependency matcher patterns {id="dependencymatcher-patterns"} From 6255e386954778ea745aef4cf9ae3c3051a643bd Mon Sep 17 00:00:00 2001 From: Sergiu Nisioi Date: Thu, 28 Sep 2023 16:06:50 +0700 Subject: [PATCH 62/74] Adding rolegal model to the spaCy universe (#13017) * adding rolegal model to the spaCy universe * Fix formatting * Use raw URL * update image url and example * fix pip and update url to raw * okay, let's add thumb instead of image :octopus: * Update website/meta/universe.json --------- Co-authored-by: Adriane Boyd --- website/meta/universe.json | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/website/meta/universe.json b/website/meta/universe.json index 46de8121c..b2868c084 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -4469,6 +4469,37 @@ }, "category": ["pipeline", "standalone"], "tags": ["spans", "rules", "ner"] + }, + { + "id": "rolegal", + "title": "A spaCy Package for Romanian Legal Document Processing", + "thumb": "https://raw.githubusercontent.com/senisioi/rolegal/main/img/paper200x200.jpeg", + "slogan": "rolegal: a spaCy Package for Noisy Romanian Legal Document Processing", + "description": "This is a spaCy language model for Romanian legal domain trained with floret 4-gram to 5-gram embeddings and `LEGAL` entity recognition. Useful for processing OCR-resulted noisy legal documents.", + "github": "senisioi/rolegal", + "pip": "ro-legal-fl", + "tags": ["legal", "floret", "ner", "romanian"], + "code_example": [ + "import spacy", + "nlp = spacy.load(\"ro_legal_fl\")", + "", + "doc = nlp(\"Titlul III din LEGEA nr. 255 din 19 iulie 2013, publicată în MONITORUL OFICIAL\")", + "# legal entity identification", + "for entity in doc.ents:", + " print('entity: ', entity, '; entity type: ', entity.label_)", + "", + "# floret n-gram embeddings robust to typos", + "print(nlp('achizit1e public@').similarity(nlp('achiziții publice')))", + "# 0.7393895566928835", + "print(nlp('achizitii publice').similarity(nlp('achiziții publice')))", + "# 0.8996480808279399" + ], + "author": "Sergiu Nisioi", + "author_links": { + "github": "senisioi", + "website": "https://nlp.unibuc.ro/people/snisioi.html" + }, + "category": ["pipeline", "training", "models"] } ], From beda27a91eadd70563dbaffd844d8c9d5e245928 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dani=C3=ABl=20de=20Kok?= Date: Thu, 28 Sep 2023 11:36:44 +0200 Subject: [PATCH 63/74] Load the cli module lazily for spacy.info (#12962) * Load the cli module lazily for spacy.info This avoids that the `spacy` module cannot be imported when the users chooses not to install `typer`/`requests`. * Add test --------- Co-authored-by: Adriane Boyd --- spacy/__init__.py | 7 ++++++- spacy/tests/test_cli.py | 4 ++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/spacy/__init__.py b/spacy/__init__.py index 1a18ad0d5..8aa2eccd7 100644 --- a/spacy/__init__.py +++ b/spacy/__init__.py @@ -13,7 +13,6 @@ from thinc.api import Config, prefer_gpu, require_cpu, require_gpu # noqa: F401 from . import pipeline # noqa: F401 from . import util from .about import __version__ # noqa: F401 -from .cli.info import info # noqa: F401 from .errors import Errors from .glossary import explain # noqa: F401 from .language import Language @@ -77,3 +76,9 @@ def blank( # We should accept both dot notation and nested dict here for consistency config = util.dot_to_dict(config) return LangClass.from_config(config, vocab=vocab, meta=meta) + + +def info(*args, **kwargs): + from .cli.info import info as cli_info + + return cli_info(*args, **kwargs) diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py index 8e1c9ca32..ebf2ec7da 100644 --- a/spacy/tests/test_cli.py +++ b/spacy/tests/test_cli.py @@ -14,6 +14,7 @@ from thinc.api import Config, ConfigValidationError import spacy from spacy import about +from spacy import info as spacy_info from spacy.cli import info from spacy.cli._util import ( download_file, @@ -225,6 +226,9 @@ def test_cli_info(): raw_data = info(tmp_dir, exclude=[""]) assert raw_data["lang"] == "nl" assert raw_data["components"] == ["textcat"] + raw_data = spacy_info(tmp_dir, exclude=[""]) + assert raw_data["lang"] == "nl" + assert raw_data["components"] == ["textcat"] def test_cli_converters_conllu_to_docs(): From 55614d6799682c9658b4249c21f47a2ae7c07fe8 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 12 Sep 2023 08:49:41 +0200 Subject: [PATCH 64/74] Add profile=False to currently unprofiled cython --- spacy/attrs.pyx | 1 + spacy/lexeme.pyx | 1 + spacy/ml/parser_model.pyx | 1 + spacy/morphology.pyx | 1 + spacy/parts_of_speech.pyx | 2 +- spacy/pipeline/_edit_tree_internals/edit_trees.pyx | 1 + spacy/pipeline/_parser_internals/_state.pyx | 1 + spacy/pipeline/_parser_internals/ner.pyx | 1 + spacy/pipeline/_parser_internals/stateclass.pyx | 1 + spacy/pipeline/_parser_internals/transition_system.pyx | 1 + spacy/pipeline/transition_parser.pyx | 1 + spacy/strings.pyx | 1 + spacy/symbols.pyx | 1 + spacy/tokens/graph.pyx | 1 + spacy/tokens/morphanalysis.pyx | 1 + spacy/tokens/span.pyx | 1 + spacy/tokens/span_group.pyx | 1 + spacy/tokens/token.pyx | 1 + spacy/training/align.pyx | 1 + spacy/training/alignment_array.pyx | 1 + spacy/training/example.pyx | 1 + spacy/training/gold_io.pyx | 1 + spacy/typedefs.pyx | 1 + 23 files changed, 23 insertions(+), 1 deletion(-) diff --git a/spacy/attrs.pyx b/spacy/attrs.pyx index 97b5d5e36..363dd094d 100644 --- a/spacy/attrs.pyx +++ b/spacy/attrs.pyx @@ -1,3 +1,4 @@ +# cython: profile=False from .errors import Errors IOB_STRINGS = ("", "I", "O", "B") diff --git a/spacy/lexeme.pyx b/spacy/lexeme.pyx index 60d22e615..f803d5e93 100644 --- a/spacy/lexeme.pyx +++ b/spacy/lexeme.pyx @@ -1,4 +1,5 @@ # cython: embedsignature=True +# cython: profile=False # Compiler crashes on memory view coercion without this. Should report bug. cimport numpy as np from libc.string cimport memset diff --git a/spacy/ml/parser_model.pyx b/spacy/ml/parser_model.pyx index ae60972aa..f004c562e 100644 --- a/spacy/ml/parser_model.pyx +++ b/spacy/ml/parser_model.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True, cdivision=True, boundscheck=False +# cython: profile=False cimport numpy as np from libc.math cimport exp from libc.stdlib cimport calloc, free, realloc diff --git a/spacy/morphology.pyx b/spacy/morphology.pyx index ecbbed729..cef45b04d 100644 --- a/spacy/morphology.pyx +++ b/spacy/morphology.pyx @@ -1,4 +1,5 @@ # cython: infer_types +# cython: profile=False import warnings import numpy diff --git a/spacy/parts_of_speech.pyx b/spacy/parts_of_speech.pyx index e71fb917f..98e3570ec 100644 --- a/spacy/parts_of_speech.pyx +++ b/spacy/parts_of_speech.pyx @@ -1,4 +1,4 @@ - +# cython: profile=False IDS = { "": NO_TAG, "ADJ": ADJ, diff --git a/spacy/pipeline/_edit_tree_internals/edit_trees.pyx b/spacy/pipeline/_edit_tree_internals/edit_trees.pyx index 78cd25622..7abd9f2a6 100644 --- a/spacy/pipeline/_edit_tree_internals/edit_trees.pyx +++ b/spacy/pipeline/_edit_tree_internals/edit_trees.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True, binding=True +# cython: profile=False from cython.operator cimport dereference as deref from libc.stdint cimport UINT32_MAX, uint32_t from libc.string cimport memset diff --git a/spacy/pipeline/_parser_internals/_state.pyx b/spacy/pipeline/_parser_internals/_state.pyx index e69de29bb..61bf62038 100644 --- a/spacy/pipeline/_parser_internals/_state.pyx +++ b/spacy/pipeline/_parser_internals/_state.pyx @@ -0,0 +1 @@ +# cython: profile=False diff --git a/spacy/pipeline/_parser_internals/ner.pyx b/spacy/pipeline/_parser_internals/ner.pyx index 6c4f8e245..e4312bd2f 100644 --- a/spacy/pipeline/_parser_internals/ner.pyx +++ b/spacy/pipeline/_parser_internals/ner.pyx @@ -1,3 +1,4 @@ +# cython: profile=False from cymem.cymem cimport Pool from libc.stdint cimport int32_t diff --git a/spacy/pipeline/_parser_internals/stateclass.pyx b/spacy/pipeline/_parser_internals/stateclass.pyx index fdb5004bb..e3b063b7d 100644 --- a/spacy/pipeline/_parser_internals/stateclass.pyx +++ b/spacy/pipeline/_parser_internals/stateclass.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True +# cython: profile=False from libcpp.vector cimport vector from ...tokens.doc cimport Doc diff --git a/spacy/pipeline/_parser_internals/transition_system.pyx b/spacy/pipeline/_parser_internals/transition_system.pyx index aabbdfa24..e035053b3 100644 --- a/spacy/pipeline/_parser_internals/transition_system.pyx +++ b/spacy/pipeline/_parser_internals/transition_system.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True +# cython: profile=False from __future__ import print_function from cymem.cymem cimport Pool diff --git a/spacy/pipeline/transition_parser.pyx b/spacy/pipeline/transition_parser.pyx index 11c8fafc7..9a278fc13 100644 --- a/spacy/pipeline/transition_parser.pyx +++ b/spacy/pipeline/transition_parser.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True, cdivision=True, boundscheck=False, binding=True +# cython: profile=False from __future__ import print_function cimport numpy as np diff --git a/spacy/strings.pyx b/spacy/strings.pyx index b0799d6fc..376a13175 100644 --- a/spacy/strings.pyx +++ b/spacy/strings.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True +# cython: profile=False cimport cython from libc.stdint cimport uint32_t from libc.string cimport memcpy diff --git a/spacy/symbols.pyx b/spacy/symbols.pyx index d1deeb0e7..f7713577b 100644 --- a/spacy/symbols.pyx +++ b/spacy/symbols.pyx @@ -1,4 +1,5 @@ # cython: optimize.unpack_method_calls=False +# cython: profile=False IDS = { "": NIL, "IS_ALPHA": IS_ALPHA, diff --git a/spacy/tokens/graph.pyx b/spacy/tokens/graph.pyx index 1cbec09f4..6c4ce6ce3 100644 --- a/spacy/tokens/graph.pyx +++ b/spacy/tokens/graph.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True, cdivision=True, boundscheck=False, binding=True +# cython: profile=False from typing import Generator, List, Tuple cimport cython diff --git a/spacy/tokens/morphanalysis.pyx b/spacy/tokens/morphanalysis.pyx index ba7c638f6..ea5d07fa4 100644 --- a/spacy/tokens/morphanalysis.pyx +++ b/spacy/tokens/morphanalysis.pyx @@ -1,3 +1,4 @@ +# cython: profile=False cimport numpy as np from libc.string cimport memset diff --git a/spacy/tokens/span.pyx b/spacy/tokens/span.pyx index cf90e416b..af3ba8db5 100644 --- a/spacy/tokens/span.pyx +++ b/spacy/tokens/span.pyx @@ -1,3 +1,4 @@ +# cython: profile=False cimport numpy as np import copy diff --git a/spacy/tokens/span_group.pyx b/spacy/tokens/span_group.pyx index d245a1425..257c907bc 100644 --- a/spacy/tokens/span_group.pyx +++ b/spacy/tokens/span_group.pyx @@ -1,3 +1,4 @@ +# cython: profile=False import struct import weakref from copy import deepcopy diff --git a/spacy/tokens/token.pyx b/spacy/tokens/token.pyx index de967ba25..9fd4118d6 100644 --- a/spacy/tokens/token.pyx +++ b/spacy/tokens/token.pyx @@ -1,4 +1,5 @@ # cython: infer_types=True +# cython: profile=False # Compiler crashes on memory view coercion without this. Should report bug. cimport numpy as np diff --git a/spacy/training/align.pyx b/spacy/training/align.pyx index 79fec73c4..c68110e30 100644 --- a/spacy/training/align.pyx +++ b/spacy/training/align.pyx @@ -1,3 +1,4 @@ +# cython: profile=False import re from itertools import chain from typing import List, Tuple diff --git a/spacy/training/alignment_array.pyx b/spacy/training/alignment_array.pyx index b0be1512b..f0eb5cf39 100644 --- a/spacy/training/alignment_array.pyx +++ b/spacy/training/alignment_array.pyx @@ -1,3 +1,4 @@ +# cython: profile=False from typing import List import numpy diff --git a/spacy/training/example.pyx b/spacy/training/example.pyx index 3f0cf5ade..abdcecf71 100644 --- a/spacy/training/example.pyx +++ b/spacy/training/example.pyx @@ -1,3 +1,4 @@ +# cython: profile=False from collections.abc import Iterable as IterableInstance import numpy diff --git a/spacy/training/gold_io.pyx b/spacy/training/gold_io.pyx index 2fc36e41f..afbdf4631 100644 --- a/spacy/training/gold_io.pyx +++ b/spacy/training/gold_io.pyx @@ -1,3 +1,4 @@ +# cython: profile=False import warnings import srsly diff --git a/spacy/typedefs.pyx b/spacy/typedefs.pyx index e69de29bb..61bf62038 100644 --- a/spacy/typedefs.pyx +++ b/spacy/typedefs.pyx @@ -0,0 +1 @@ +# cython: profile=False From 538304948e6ec9a92411a9c1b0386012cf4dafc3 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 12 Sep 2023 08:50:01 +0200 Subject: [PATCH 65/74] Remove profile=True from currently profiled cython --- spacy/kb/candidate.pyx | 2 +- spacy/kb/kb.pyx | 2 +- spacy/kb/kb_in_memory.pyx | 2 +- spacy/matcher/dependencymatcher.pyx | 2 +- spacy/matcher/levenshtein.pyx | 2 +- spacy/matcher/matcher.pyx | 2 +- spacy/matcher/phrasematcher.pyx | 2 +- spacy/pipeline/_parser_internals/_beam_utils.pyx | 1 - spacy/pipeline/_parser_internals/arc_eager.pyx | 2 +- spacy/pipeline/_parser_internals/nonproj.pyx | 2 +- spacy/pipeline/dep_parser.pyx | 2 +- spacy/pipeline/morphologizer.pyx | 2 +- spacy/pipeline/multitask.pyx | 2 +- spacy/pipeline/ner.pyx | 2 +- spacy/pipeline/pipe.pyx | 2 +- spacy/pipeline/sentencizer.pyx | 2 +- spacy/pipeline/senter.pyx | 2 +- spacy/pipeline/tagger.pyx | 2 +- spacy/pipeline/trainable_pipe.pyx | 2 +- spacy/tokenizer.pyx | 2 +- spacy/tokens/_retokenize.pyx | 2 +- spacy/tokens/doc.pyx | 2 +- spacy/vectors.pyx | 2 +- spacy/vocab.pyx | 1 - 24 files changed, 22 insertions(+), 24 deletions(-) diff --git a/spacy/kb/candidate.pyx b/spacy/kb/candidate.pyx index 53fc9b036..4369676e2 100644 --- a/spacy/kb/candidate.pyx +++ b/spacy/kb/candidate.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True +# cython: infer_types=True from typing import Iterable diff --git a/spacy/kb/kb.pyx b/spacy/kb/kb.pyx index 6ad4c3564..c7db34e16 100644 --- a/spacy/kb/kb.pyx +++ b/spacy/kb/kb.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True +# cython: infer_types=True from pathlib import Path from typing import Iterable, Tuple, Union diff --git a/spacy/kb/kb_in_memory.pyx b/spacy/kb/kb_in_memory.pyx index 02773cbae..2b21f246a 100644 --- a/spacy/kb/kb_in_memory.pyx +++ b/spacy/kb/kb_in_memory.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True +# cython: infer_types=True from typing import Any, Callable, Dict, Iterable import srsly diff --git a/spacy/matcher/dependencymatcher.pyx b/spacy/matcher/dependencymatcher.pyx index 1f66d99b2..ab5f5d5d1 100644 --- a/spacy/matcher/dependencymatcher.pyx +++ b/spacy/matcher/dependencymatcher.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True +# cython: infer_types=True import warnings from collections import defaultdict from itertools import product diff --git a/spacy/matcher/levenshtein.pyx b/spacy/matcher/levenshtein.pyx index e823ce99d..e394f2cf4 100644 --- a/spacy/matcher/levenshtein.pyx +++ b/spacy/matcher/levenshtein.pyx @@ -1,4 +1,4 @@ -# cython: profile=True, binding=True, infer_types=True +# cython: binding=True, infer_types=True from cpython.object cimport PyObject from libc.stdint cimport int64_t diff --git a/spacy/matcher/matcher.pyx b/spacy/matcher/matcher.pyx index 167f85af4..9a9ed4212 100644 --- a/spacy/matcher/matcher.pyx +++ b/spacy/matcher/matcher.pyx @@ -1,4 +1,4 @@ -# cython: binding=True, infer_types=True, profile=True +# cython: binding=True, infer_types=True from typing import Iterable, List from cymem.cymem cimport Pool diff --git a/spacy/matcher/phrasematcher.pyx b/spacy/matcher/phrasematcher.pyx index 26633e6d6..4efcdb05c 100644 --- a/spacy/matcher/phrasematcher.pyx +++ b/spacy/matcher/phrasematcher.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True +# cython: infer_types=True from preshed.maps cimport map_clear, map_get, map_init, map_iter, map_set import warnings diff --git a/spacy/pipeline/_parser_internals/_beam_utils.pyx b/spacy/pipeline/_parser_internals/_beam_utils.pyx index de8f0bf7b..ac04be5a7 100644 --- a/spacy/pipeline/_parser_internals/_beam_utils.pyx +++ b/spacy/pipeline/_parser_internals/_beam_utils.pyx @@ -1,5 +1,4 @@ # cython: infer_types=True -# cython: profile=True import numpy from thinc.extra.search cimport Beam diff --git a/spacy/pipeline/_parser_internals/arc_eager.pyx b/spacy/pipeline/_parser_internals/arc_eager.pyx index bcb4626fb..e13754944 100644 --- a/spacy/pipeline/_parser_internals/arc_eager.pyx +++ b/spacy/pipeline/_parser_internals/arc_eager.pyx @@ -1,4 +1,4 @@ -# cython: profile=True, cdivision=True, infer_types=True +# cython: cdivision=True, infer_types=True from cymem.cymem cimport Address, Pool from libc.stdint cimport int32_t from libcpp.vector cimport vector diff --git a/spacy/pipeline/_parser_internals/nonproj.pyx b/spacy/pipeline/_parser_internals/nonproj.pyx index 93ad14feb..7de19851e 100644 --- a/spacy/pipeline/_parser_internals/nonproj.pyx +++ b/spacy/pipeline/_parser_internals/nonproj.pyx @@ -1,4 +1,4 @@ -# cython: profile=True, infer_types=True +# cython: infer_types=True """Implements the projectivize/deprojectivize mechanism in Nivre & Nilsson 2005 for doing pseudo-projective parsing implementation uses the HEAD decoration scheme. diff --git a/spacy/pipeline/dep_parser.pyx b/spacy/pipeline/dep_parser.pyx index 57f091788..18a220bd6 100644 --- a/spacy/pipeline/dep_parser.pyx +++ b/spacy/pipeline/dep_parser.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from collections import defaultdict from typing import Callable, Optional diff --git a/spacy/pipeline/morphologizer.pyx b/spacy/pipeline/morphologizer.pyx index 7ca3908bd..d415ae43c 100644 --- a/spacy/pipeline/morphologizer.pyx +++ b/spacy/pipeline/morphologizer.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from itertools import islice from typing import Callable, Dict, Optional, Union diff --git a/spacy/pipeline/multitask.pyx b/spacy/pipeline/multitask.pyx index 2a62a50d5..f33a90fde 100644 --- a/spacy/pipeline/multitask.pyx +++ b/spacy/pipeline/multitask.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from typing import Optional import numpy diff --git a/spacy/pipeline/ner.pyx b/spacy/pipeline/ner.pyx index 15c092ae9..bb009dc7a 100644 --- a/spacy/pipeline/ner.pyx +++ b/spacy/pipeline/ner.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from collections import defaultdict from typing import Callable, Optional diff --git a/spacy/pipeline/pipe.pyx b/spacy/pipeline/pipe.pyx index 90775c465..72ea7e45a 100644 --- a/spacy/pipeline/pipe.pyx +++ b/spacy/pipeline/pipe.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True import warnings from typing import Callable, Dict, Iterable, Iterator, Tuple, Union diff --git a/spacy/pipeline/sentencizer.pyx b/spacy/pipeline/sentencizer.pyx index 76f296644..08ba9d989 100644 --- a/spacy/pipeline/sentencizer.pyx +++ b/spacy/pipeline/sentencizer.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from typing import Callable, List, Optional import srsly diff --git a/spacy/pipeline/senter.pyx b/spacy/pipeline/senter.pyx index 37ddcc3c0..df093baa9 100644 --- a/spacy/pipeline/senter.pyx +++ b/spacy/pipeline/senter.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from itertools import islice from typing import Callable, Optional diff --git a/spacy/pipeline/tagger.pyx b/spacy/pipeline/tagger.pyx index 4c5265a78..34e85d49c 100644 --- a/spacy/pipeline/tagger.pyx +++ b/spacy/pipeline/tagger.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from itertools import islice from typing import Callable, Optional diff --git a/spacy/pipeline/trainable_pipe.pyx b/spacy/pipeline/trainable_pipe.pyx index e5865e070..8f219b327 100644 --- a/spacy/pipeline/trainable_pipe.pyx +++ b/spacy/pipeline/trainable_pipe.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple import srsly diff --git a/spacy/tokenizer.pyx b/spacy/tokenizer.pyx index 8fc95bea0..a239eaf45 100644 --- a/spacy/tokenizer.pyx +++ b/spacy/tokenizer.pyx @@ -1,4 +1,4 @@ -# cython: embedsignature=True, profile=True, binding=True +# cython: embedsignature=True, binding=True cimport cython from cymem.cymem cimport Pool from cython.operator cimport dereference as deref diff --git a/spacy/tokens/_retokenize.pyx b/spacy/tokens/_retokenize.pyx index f28d2e088..b0e4ff85c 100644 --- a/spacy/tokens/_retokenize.pyx +++ b/spacy/tokens/_retokenize.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, bounds_check=False, profile=True +# cython: infer_types=True, bounds_check=False from cymem.cymem cimport Pool from libc.string cimport memset diff --git a/spacy/tokens/doc.pyx b/spacy/tokens/doc.pyx index 8fc2c4b3c..745eb5ff3 100644 --- a/spacy/tokens/doc.pyx +++ b/spacy/tokens/doc.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, bounds_check=False, profile=True +# cython: infer_types=True, bounds_check=False from typing import Set cimport cython diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx index 2817bcad4..6ff99bb59 100644 --- a/spacy/vectors.pyx +++ b/spacy/vectors.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, profile=True, binding=True +# cython: infer_types=True, binding=True from typing import Callable from cython.operator cimport dereference as deref diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx index 48e8fcb90..4004a70e0 100644 --- a/spacy/vocab.pyx +++ b/spacy/vocab.pyx @@ -1,4 +1,3 @@ -# cython: profile=True import functools import numpy From 1adf79414e14f8f45a64c7cdb6cb098b4cf1f46f Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 12 Sep 2023 08:52:15 +0200 Subject: [PATCH 66/74] Set cython profiling default to True for <3.12, False for >=3.12 --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 3b6fae37b..33178662d 100755 --- a/setup.py +++ b/setup.py @@ -78,6 +78,7 @@ COMPILER_DIRECTIVES = { "language_level": -3, "embedsignature": True, "annotation_typing": False, + "profile": sys.version_info < (3, 12), } # Files to copy into the package that are otherwise not included COPY_FILES = { From 76d94b31f239f419fabfe6fd27bb039175a6bee5 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 22 Sep 2023 16:58:33 +0200 Subject: [PATCH 67/74] Branch on python 3.12+ shutil.rmtree in make_tempdir --- spacy/util.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/spacy/util.py b/spacy/util.py index c5c57d67d..8464e411f 100644 --- a/spacy/util.py +++ b/spacy/util.py @@ -1068,7 +1068,10 @@ def make_tempdir() -> Generator[Path, None, None]: rmfunc(path) try: - shutil.rmtree(str(d), onerror=force_remove) + if sys.version_info >= (3, 12): + shutil.rmtree(str(d), onexc=force_remove) + else: + shutil.rmtree(str(d), onerror=force_remove) except PermissionError as e: warnings.warn(Warnings.W091.format(dir=d, msg=e)) From b4990395f9bff384b5617ee8ad861a1e0f71cf01 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 22 Sep 2023 17:13:49 +0200 Subject: [PATCH 68/74] Update mypy requirements --- .github/workflows/tests.yml | 1 + requirements.txt | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index f68280be2..a42803a61 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -93,6 +93,7 @@ jobs: - name: Run mypy run: | python -m mypy spacy + if: matrix.python_version != '3.7' - name: Delete source directory and .egg-info run: | diff --git a/requirements.txt b/requirements.txt index 48d188ec9..a8ba956a1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -33,7 +33,7 @@ pytest-timeout>=1.3.0,<2.0.0 mock>=2.0.0,<3.0.0 flake8>=3.8.0,<6.0.0 hypothesis>=3.27.0,<7.0.0 -mypy>=0.990,<1.1.0; platform_machine != "aarch64" +mypy>=1.5.0,<1.6.0; platform_machine != "aarch64" and python_version >= "3.8" types-mock>=0.1.1 types-setuptools>=57.0.0 types-requests From 467c82439e20a7e0b54cdce1fff6ceb63a237c63 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 22 Sep 2023 18:20:22 +0200 Subject: [PATCH 69/74] Always use tqdm with `disable=None` `tqdm` can cause deadlocks in the test suite if enabled. --- spacy/cli/apply.py | 4 +++- spacy/cli/benchmark_speed.py | 2 +- spacy/cli/profile.py | 2 +- spacy/training/initialize.py | 2 +- 4 files changed, 6 insertions(+), 4 deletions(-) diff --git a/spacy/cli/apply.py b/spacy/cli/apply.py index 8c4b4c8bf..ffd810506 100644 --- a/spacy/cli/apply.py +++ b/spacy/cli/apply.py @@ -133,7 +133,9 @@ def apply( if len(text_files) > 0: streams.append(_stream_texts(text_files)) datagen = cast(DocOrStrStream, chain(*streams)) - for doc in tqdm.tqdm(nlp.pipe(datagen, batch_size=batch_size, n_process=n_process)): + for doc in tqdm.tqdm( + nlp.pipe(datagen, batch_size=batch_size, n_process=n_process), disable=None + ): docbin.add(doc) if output_file.suffix == "": output_file = output_file.with_suffix(".spacy") diff --git a/spacy/cli/benchmark_speed.py b/spacy/cli/benchmark_speed.py index a683d1591..c7fd771c3 100644 --- a/spacy/cli/benchmark_speed.py +++ b/spacy/cli/benchmark_speed.py @@ -89,7 +89,7 @@ class Quartiles: def annotate( nlp: Language, docs: List[Doc], batch_size: Optional[int] ) -> numpy.ndarray: - docs = nlp.pipe(tqdm(docs, unit="doc"), batch_size=batch_size) + docs = nlp.pipe(tqdm(docs, unit="doc", disable=None), batch_size=batch_size) wps = [] while True: with time_context() as elapsed: diff --git a/spacy/cli/profile.py b/spacy/cli/profile.py index e1f720327..e5b8f1193 100644 --- a/spacy/cli/profile.py +++ b/spacy/cli/profile.py @@ -71,7 +71,7 @@ def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) -> def parse_texts(nlp: Language, texts: Sequence[str]) -> None: - for doc in nlp.pipe(tqdm.tqdm(texts), batch_size=16): + for doc in nlp.pipe(tqdm.tqdm(texts, disable=None), batch_size=16): pass diff --git a/spacy/training/initialize.py b/spacy/training/initialize.py index 82d4ebf24..062170221 100644 --- a/spacy/training/initialize.py +++ b/spacy/training/initialize.py @@ -302,7 +302,7 @@ def read_vectors( shape = (truncate_vectors, shape[1]) vectors_data = numpy.zeros(shape=shape, dtype="f") vectors_keys = [] - for i, line in enumerate(tqdm.tqdm(f)): + for i, line in enumerate(tqdm.tqdm(f, disable=None)): line = line.rstrip() pieces = line.rsplit(" ", vectors_data.shape[1]) word = pieces.pop(0) From 78504c25a516eace9702c8f1bf9dafb82b6b1b2b Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Fri, 22 Sep 2023 11:54:15 +0200 Subject: [PATCH 70/74] CI: Add python 3.12.0rc2 --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index a42803a61..1058b4673 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -58,7 +58,7 @@ jobs: fail-fast: true matrix: os: [ubuntu-latest, windows-latest, macos-latest] - python_version: ["3.11"] + python_version: ["3.11", "3.12.0-rc.2"] include: - os: windows-latest python_version: "3.7" From 6b4f774418d2ac771658bc0122a3e97e1fce9085 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Thu, 28 Sep 2023 21:27:42 +0200 Subject: [PATCH 71/74] Set version to v3.7.0 (#13028) --- spacy/about.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spacy/about.py b/spacy/about.py index d816926fd..1a3367673 100644 --- a/spacy/about.py +++ b/spacy/about.py @@ -1,5 +1,5 @@ # fmt: off __title__ = "spacy" -__version__ = "3.7.0.dev0" +__version__ = "3.7.0" __download_url__ = "https://github.com/explosion/spacy-models/releases/download" __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json" From 483d4a5bc0762f8d942f20b5ae58010ce73cb423 Mon Sep 17 00:00:00 2001 From: Matthew Hoffman Date: Thu, 28 Sep 2023 23:22:56 -0700 Subject: [PATCH 72/74] Allow spacy-transformers v1.3.x in transformers extra (#13025) Co-authored-by: Adriane Boyd --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 852ff4049..75f2e3a15 100644 --- a/setup.cfg +++ b/setup.cfg @@ -78,7 +78,7 @@ console_scripts = lookups = spacy_lookups_data>=1.0.3,<1.1.0 transformers = - spacy_transformers>=1.1.2,<1.3.0 + spacy_transformers>=1.1.2,<1.4.0 cuda = cupy>=5.0.0b4,<13.0.0 cuda80 = From 1b043dde3fc674869f11b8b138db878552b4c91a Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Wed, 9 Aug 2023 13:43:50 +0200 Subject: [PATCH 73/74] Revert "disable tests until 3.7 models are available" This reverts commit 991bcc111e1a35cc96dba32ac08c212b0b360384. --- .github/workflows/tests.yml | 54 ++++++++++++++++++------------------- spacy/tests/test_cli.py | 2 -- 2 files changed, 27 insertions(+), 29 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1058b4673..976b1f4f2 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -115,22 +115,22 @@ jobs: - name: Test import run: python -W error -c "import spacy" -# - name: "Test download CLI" -# run: | -# python -m spacy download ca_core_news_sm -# python -m spacy download ca_core_news_md -# python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')" -# if: matrix.python_version == '3.9' -# -# - name: "Test download_url in info CLI" -# run: | -# python -W error -m spacy info ca_core_news_sm | grep -q download_url -# if: matrix.python_version == '3.9' -# -# - name: "Test no warnings on load (#11713)" -# run: | -# python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')" -# if: matrix.python_version == '3.9' + - name: "Test download CLI" + run: | + python -m spacy download ca_core_news_sm + python -m spacy download ca_core_news_md + python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')" + if: matrix.python_version == '3.9' + + - name: "Test download_url in info CLI" + run: | + python -W error -m spacy info ca_core_news_sm | grep -q download_url + if: matrix.python_version == '3.9' + + - name: "Test no warnings on load (#11713)" + run: | + python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')" + if: matrix.python_version == '3.9' - name: "Test convert CLI" run: | @@ -154,17 +154,17 @@ jobs: python -m spacy train ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy --training.max_steps 10 --gpu-id -1 if: matrix.python_version == '3.9' -# - name: "Test assemble CLI" -# run: | -# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')" -# PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir -# if: matrix.python_version == '3.9' -# -# - name: "Test assemble CLI vectors warning" -# run: | -# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')" -# python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113 -# if: matrix.python_version == '3.9' + - name: "Test assemble CLI" + run: | + python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')" + PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir + if: matrix.python_version == '3.9' + + - name: "Test assemble CLI vectors warning" + run: | + python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')" + python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113 + if: matrix.python_version == '3.9' - name: "Install test requirements" run: | diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py index 8c1d1a64c..0d2fe0a9e 100644 --- a/spacy/tests/test_cli.py +++ b/spacy/tests/test_cli.py @@ -538,7 +538,6 @@ def test_string_to_list_intify(value): assert string_to_list(value, intify=True) == [1, 2, 3] -@pytest.mark.skip(reason="Temporarily skip before 3.7 models are published") def test_download_compatibility(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False @@ -549,7 +548,6 @@ def test_download_compatibility(): assert get_minor_version(about.__version__) == get_minor_version(version) -@pytest.mark.skip(reason="Temporarily skip before 3.7 models are published") def test_validate_compatibility_table(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False From 160e61772e3e4fbd4e9e28446c6d687596921f93 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Sun, 1 Oct 2023 21:40:07 +0200 Subject: [PATCH 74/74] Docs for v3.7.0 (#13029) * Docs for v3.7.0 * Minor fixes * Extend Weasel notes * Minor edits * Update version in README --- README.md | 2 +- website/docs/usage/v3-7.mdx | 140 +++++++++++++++++++++++++++++++++ website/meta/sidebars.json | 3 +- website/src/templates/index.js | 4 +- 4 files changed, 145 insertions(+), 4 deletions(-) create mode 100644 website/docs/usage/v3-7.mdx diff --git a/README.md b/README.md index 3920c1dc2..b2ffa4639 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE). -💫 **Version 3.6 out now!** +💫 **Version 3.7 out now!** [Check out the release notes here.](https://github.com/explosion/spaCy/releases) [![tests](https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/spaCy/actions/workflows/tests.yml) diff --git a/website/docs/usage/v3-7.mdx b/website/docs/usage/v3-7.mdx new file mode 100644 index 000000000..76fc9530f --- /dev/null +++ b/website/docs/usage/v3-7.mdx @@ -0,0 +1,140 @@ +--- +title: What's New in v3.7 +teaser: New features and how to upgrade +menu: + - ['New Features', 'features'] + - ['Upgrading Notes', 'upgrading'] +--- + +## New features {id="features",hidden="true"} + +spaCy v3.7 adds support for Python 3.12, introduces the new standalone library +[Weasel](https://github.com/explosion/weasel) for project workflows, and updates +the transformer-based trained pipelines to use our new +[Curated Transformers](https://github.com/explosion/curated-transformers) +library. + +This release drops support for Python 3.6. + +### Weasel {id="weasel"} + +The [spaCy projects](/usage/projects) functionality has been moved into a new +standalone library [Weasel](https://github.com/explosion/weasel). This brings +minor changes to spaCy-specific settings in spaCy projects (see +[upgrading](#upgrading) below), but also makes it possible to use the same +workflow functionality outside of spaCy. + +All `spacy project` commands should run as before, just now they're using Weasel +under the hood. + + + +Remote storage for spaCy projects is not yet supported for Python 3.12. Use +Python 3.11 or earlier for remote storage. + + + +### Registered vectors {id="custom-vectors"} + +You can specify a custom registered vectors class under `[nlp.vectors]` in order +to use static vectors in formats other than the ones supported by +[`Vectors`](/api/vectors). To implement your custom vectors, extend the abstract +class [`BaseVectors`](/api/basevectors). See an example using +[BPEmb subword embeddings](/usage/embeddings-transformers#custom-vectors). + +### Additional features and improvements {id="additional-features-and-improvements"} + +- Add support for Python 3.12. +- Extend to Thinc v8.2. +- Extend `transformers` extra to `spacy-transformers` v1.3. +- Add `--spans-key` option for CLI evaluation with `spacy benchmark accuracy`. +- Load the CLI module lazily for `spacy.info`. +- Add type stubs for for `spacy.training.example`. +- Warn for unsupported pattern keys in dependency matcher. +- `Language.replace_listeners`: Pass the replaced listener and the `tok2vec` + pipe to the callback in order to support `spacy-curated-transformers`. +- Always use `tqdm` with `disable=None` in order to disable output in + non-interactive environments. +- Language updates: + - Add left and right pointing angle brackets as punctuation to ancient Greek. + - Update example sentences for Turkish. +- Package setup updates: + - Update NumPy build constraints for NumPy 1.25+. For Python 3.9+, it is no + longer necessary to set build constraints while building binary wheels. + - Refactor Cython profiling in order to disable profiling for Python 3.12 in + the package setup, since Cython does not currently support profiling for + Python 3.12. + +## Trained pipelines {id="pipelines"} + +### Pipeline updates {id="pipeline-updates"} + +The transformer-based `trf` pipelines have been updated to use our new +[Curated Transformers](https://github.com/explosion/curated-transformers) +library using the Thinc model wrappers and pipeline component from +[spaCy Curated Transformers](https://github.com/explosion/spacy-curated-transformers). + +## Notes about upgrading from v3.6 {id="upgrading"} + +This release drops support for Python 3.6, drops mypy checks for Python 3.7 and +removes the `ray` extra. In addition there are several minor changes for spaCy +projects described in the following section. + +### Backwards incompatibilities for spaCy Projects {id="upgrading-projects"} + +`spacy project` has a few backwards incompatibilities due to the transition to +the standalone library [Weasel](https://github.com/explosion/weasel), which is +not as tightly coupled to spaCy. Weasel produces warnings when it detects older +spaCy-specific settings in your environment or project config. + +- Support for the `spacy_version` configuration key has been dropped. +- Support for the `check_requirements` configuration key has been dropped due to + the deprecation of `pkg_resources`. +- The `SPACY_CONFIG_OVERRIDES` environment variable is no longer checked. You + can set configuration overrides using `WEASEL_CONFIG_OVERRIDES`. +- Support for `SPACY_PROJECT_USE_GIT_VERSION` environment variable has been + dropped. +- Error codes are now Weasel-specific and do not follow spaCy error codes. + +### Pipeline package version compatibility {id="version-compat"} + +> #### Using legacy implementations +> +> In spaCy v3, you'll still be able to load and reference legacy implementations +> via [`spacy-legacy`](https://github.com/explosion/spacy-legacy), even if the +> components or architectures change and newer versions are available in the +> core library. + +When you're loading a pipeline package trained with an earlier version of spaCy +v3, you will see a warning telling you that the pipeline may be incompatible. +This doesn't necessarily have to be true, but we recommend running your +pipelines against your test suite or evaluation data to make sure there are no +unexpected results. + +If you're using one of the [trained pipelines](/models) we provide, you should +run [`spacy download`](/api/cli#download) to update to the latest version. To +see an overview of all installed packages and their compatibility, you can run +[`spacy validate`](/api/cli#validate). + +If you've trained your own custom pipeline and you've confirmed that it's still +working as expected, you can update the spaCy version requirements in the +[`meta.json`](/api/data-formats#meta): + +```diff +- "spacy_version": ">=3.6.0,<3.7.0", ++ "spacy_version": ">=3.6.0,<3.8.0", +``` + +### Updating v3.6 configs + +To update a config from spaCy v3.6 with the new v3.7 settings, run +[`init fill-config`](/api/cli#init-fill-config): + +```cli +$ python -m spacy init fill-config config-v3.6.cfg config-v3.7.cfg +``` + +In many cases ([`spacy train`](/api/cli#train), +[`spacy.load`](/api/top-level#spacy.load)), the new defaults will be filled in +automatically, but you'll need to fill in the new settings to run +[`debug config`](/api/cli#debug) and [`debug data`](/api/cli#debug-data). diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json index 617473cb0..24213ed12 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -15,7 +15,8 @@ { "text": "New in v3.3", "url": "/usage/v3-3" }, { "text": "New in v3.4", "url": "/usage/v3-4" }, { "text": "New in v3.5", "url": "/usage/v3-5" }, - { "text": "New in v3.6", "url": "/usage/v3-6" } + { "text": "New in v3.6", "url": "/usage/v3-6" }, + { "text": "New in v3.7", "url": "/usage/v3-7" } ] }, { diff --git a/website/src/templates/index.js b/website/src/templates/index.js index c8295593c..1c969bd39 100644 --- a/website/src/templates/index.js +++ b/website/src/templates/index.js @@ -58,8 +58,8 @@ const AlertSpace = ({ nightly, legacy }) => { } const navAlert = ( - - 💥 Out now: spaCy v3.6 + + 💥 Out now: spaCy v3.7 )