diff --git a/.github/azure-steps.yml b/.github/azure-steps.yml index b2bc80dd6..2f77706b8 100644 --- a/.github/azure-steps.yml +++ b/.github/azure-steps.yml @@ -52,12 +52,17 @@ steps: python -W error -c "import spacy" displayName: "Test import" - - script: | - 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')" - displayName: 'Test download CLI' - condition: eq(variables['python_version'], '3.8') +# - script: | +# 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')" +# displayName: 'Test download CLI' +# condition: eq(variables['python_version'], '3.8') +# +# - script: | +# python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')" +# displayName: 'Test no warnings on load (#11713)' +# condition: eq(variables['python_version'], '3.8') - script: | python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json . @@ -81,17 +86,17 @@ steps: displayName: 'Test train CLI' condition: eq(variables['python_version'], '3.8') - - script: | - 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 - displayName: 'Test assemble CLI' - condition: eq(variables['python_version'], '3.8') - - - script: | - 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 - displayName: 'Test assemble CLI vectors warning' - condition: eq(variables['python_version'], '3.8') +# - script: | +# 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 +# displayName: 'Test assemble CLI' +# condition: eq(variables['python_version'], '3.8') +# +# - script: | +# 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 +# displayName: 'Test assemble CLI vectors warning' +# condition: eq(variables['python_version'], '3.8') - script: | python -m pip install -U -r requirements.txt diff --git a/.github/workflows/spacy_universe_alert.yml b/.github/workflows/spacy_universe_alert.yml index f507e0594..837aaeb33 100644 --- a/.github/workflows/spacy_universe_alert.yml +++ b/.github/workflows/spacy_universe_alert.yml @@ -19,6 +19,8 @@ jobs: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 + with: + python-version: '3.10' - name: Install Bernadette app dependency and send an alert env: SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index df59697b1..e2c5e98fd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,7 +5,7 @@ repos: - id: black language_version: python3.7 additional_dependencies: ['click==8.0.4'] -- repo: https://gitlab.com/pycqa/flake8 +- repo: https://github.com/pycqa/flake8 rev: 5.0.4 hooks: - id: flake8 diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 3499042cb..0f7ea91f9 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -41,7 +41,7 @@ jobs: matrix: # We're only running one platform per Python version to speed up builds Python36Linux: - imageName: "ubuntu-latest" + imageName: "ubuntu-20.04" python.version: "3.6" # Python36Windows: # imageName: "windows-latest" @@ -50,7 +50,7 @@ jobs: # imageName: "macos-latest" # python.version: "3.6" # Python37Linux: - # imageName: "ubuntu-latest" + # imageName: "ubuntu-20.04" # python.version: "3.7" Python37Windows: imageName: "windows-latest" @@ -87,13 +87,13 @@ jobs: # python.version: "3.10" Python311Linux: imageName: 'ubuntu-latest' - python.version: '3.11.0' + python.version: '3.11' Python311Windows: imageName: 'windows-latest' - python.version: '3.11.0' + python.version: '3.11' Python311Mac: imageName: 'macos-latest' - python.version: '3.11.0' + python.version: '3.11' maxParallel: 4 pool: vmImage: $(imageName) diff --git a/requirements.txt b/requirements.txt index 9d6bbb2c4..778c05e21 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,8 +9,9 @@ murmurhash>=0.28.0,<1.1.0 wasabi>=0.9.1,<1.1.0 srsly>=2.4.3,<3.0.0 catalogue>=2.0.6,<2.1.0 -typer>=0.3.0,<0.5.0 -pathy>=0.3.5 +typer>=0.3.0,<0.8.0 +pathy>=0.10.0 +smart-open>=5.2.1,<7.0.0 # Third party dependencies numpy>=1.15.0 requests>=2.13.0,<3.0.0 @@ -30,7 +31,7 @@ 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>=0.980,<0.990; platform_machine != "aarch64" and python_version >= "3.7" +mypy>=0.990,<0.1000; platform_machine != "aarch64" and python_version >= "3.7" types-dataclasses>=0.1.3; python_version < "3.7" types-mock>=0.1.1 types-setuptools>=57.0.0 diff --git a/setup.cfg b/setup.cfg index 587af7e64..3c1bf5b0b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -43,8 +43,9 @@ install_requires = srsly>=2.4.3,<3.0.0 catalogue>=2.0.6,<2.1.0 # Third-party dependencies - typer>=0.3.0,<0.5.0 - pathy>=0.3.5 + typer>=0.3.0,<0.8.0 + pathy>=0.10.0 + smart-open>=5.2.1,<7.0.0 tqdm>=4.38.0,<5.0.0 numpy>=1.15.0 requests>=2.13.0,<3.0.0 diff --git a/spacy/about.py b/spacy/about.py index ce86e6294..640e9e93b 100644 --- a/spacy/about.py +++ b/spacy/about.py @@ -1,6 +1,6 @@ # fmt: off __title__ = "spacy" -__version__ = "3.4.2" +__version__ = "3.5.0" __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" diff --git a/spacy/cli/__init__.py b/spacy/cli/__init__.py index ce76ef9a9..aab2c8d12 100644 --- a/spacy/cli/__init__.py +++ b/spacy/cli/__init__.py @@ -27,6 +27,7 @@ from .project.dvc import project_update_dvc # noqa: F401 from .project.push import project_push # noqa: F401 from .project.pull import project_pull # noqa: F401 from .project.document import project_document # noqa: F401 +from .find_threshold import find_threshold # noqa: F401 @app.command("link", no_args_is_help=True, deprecated=True, hidden=True) diff --git a/spacy/cli/_util.py b/spacy/cli/_util.py index 897964a88..7ce006108 100644 --- a/spacy/cli/_util.py +++ b/spacy/cli/_util.py @@ -23,7 +23,7 @@ from ..util import is_compatible_version, SimpleFrozenDict, ENV_VARS from .. import about if TYPE_CHECKING: - from pathy import Pathy # noqa: F401 + from pathy import FluidPath # noqa: F401 SDIST_SUFFIX = ".tar.gz" @@ -331,7 +331,7 @@ 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, "Pathy"]) -> None: +def upload_file(src: Path, dest: Union[str, "FluidPath"]) -> None: """Upload a file. src (Path): The source path. @@ -339,13 +339,20 @@ def upload_file(src: Path, dest: Union[str, "Pathy"]) -> None: """ 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, "Pathy"], dest: Path, *, force: bool = False) -> None: +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. @@ -358,7 +365,7 @@ def download_file(src: Union[str, "Pathy"], dest: Path, *, force: bool = False) if dest.exists() and not force: return None src = str(src) - with smart_open.open(src, mode="rb", ignore_ext=True) as input_file: + 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) @@ -368,7 +375,7 @@ def ensure_pathy(path): slow and annoying Google Cloud warning).""" from pathy import Pathy # noqa: F811 - return Pathy(path) + return Pathy.fluid(path) def git_checkout( diff --git a/spacy/cli/debug_data.py b/spacy/cli/debug_data.py index 963d5b926..a85324e87 100644 --- a/spacy/cli/debug_data.py +++ b/spacy/cli/debug_data.py @@ -13,6 +13,7 @@ from ._util import import_code, debug_cli, _format_number from ..training import Example, remove_bilu_prefix from ..training.initialize import get_sourced_components from ..schemas import ConfigSchemaTraining +from ..pipeline import TrainablePipe from ..pipeline._parser_internals import nonproj from ..pipeline._parser_internals.nonproj import DELIMITER from ..pipeline import Morphologizer, SpanCategorizer @@ -934,6 +935,7 @@ def _get_labels_from_model(nlp: Language, factory_name: str) -> Set[str]: labels: Set[str] = set() for pipe_name in pipe_names: pipe = nlp.get_pipe(pipe_name) + assert isinstance(pipe, TrainablePipe) labels.update(pipe.labels) return labels diff --git a/spacy/cli/find_threshold.py b/spacy/cli/find_threshold.py new file mode 100644 index 000000000..efa664832 --- /dev/null +++ b/spacy/cli/find_threshold.py @@ -0,0 +1,233 @@ +import functools +import operator +from pathlib import Path +import logging +from typing import Optional, Tuple, Any, Dict, List + +import numpy +import wasabi.tables + +from ..pipeline import TextCategorizer, MultiLabel_TextCategorizer +from ..errors import Errors +from ..training import Corpus +from ._util import app, Arg, Opt, import_code, setup_gpu +from .. import util + +_DEFAULTS = { + "n_trials": 11, + "use_gpu": -1, + "gold_preproc": False, +} + + +@app.command( + "find-threshold", + context_settings={"allow_extra_args": False, "ignore_unknown_options": True}, +) +def find_threshold_cli( + # fmt: off + model: str = Arg(..., help="Model name or path"), + data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True), + pipe_name: str = Arg(..., help="Name of pipe to examine thresholds for"), + threshold_key: str = Arg(..., help="Key of threshold attribute in component's configuration"), + scores_key: str = Arg(..., help="Metric to optimize"), + n_trials: int = Opt(_DEFAULTS["n_trials"], "--n_trials", "-n", help="Number of trials to determine optimal thresholds"), + code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"), + use_gpu: int = Opt(_DEFAULTS["use_gpu"], "--gpu-id", "-g", help="GPU ID or -1 for CPU"), + gold_preproc: bool = Opt(_DEFAULTS["gold_preproc"], "--gold-preproc", "-G", help="Use gold preprocessing"), + verbose: bool = Opt(False, "--silent", "-V", "-VV", help="Display more information for debugging purposes"), + # fmt: on +): + """ + Runs prediction trials for a trained model with varying tresholds to maximize + the specified metric. The search space for the threshold is traversed linearly + from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout` + (the corresponding API call to `spacy.cli.find_threshold.find_threshold()` + returns all results). + + This is applicable only for components whose predictions are influenced by + thresholds - e.g. `textcat_multilabel` and `spancat`, but not `textcat`. Note + that the full path to the corresponding threshold attribute in the config has to + be provided. + + DOCS: https://spacy.io/api/cli#find-threshold + """ + + util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) + import_code(code_path) + find_threshold( + model=model, + data_path=data_path, + pipe_name=pipe_name, + threshold_key=threshold_key, + scores_key=scores_key, + n_trials=n_trials, + use_gpu=use_gpu, + gold_preproc=gold_preproc, + silent=False, + ) + + +def find_threshold( + model: str, + data_path: Path, + pipe_name: str, + threshold_key: str, + scores_key: str, + *, + n_trials: int = _DEFAULTS["n_trials"], # type: ignore + use_gpu: int = _DEFAULTS["use_gpu"], # type: ignore + gold_preproc: bool = _DEFAULTS["gold_preproc"], # type: ignore + silent: bool = True, +) -> Tuple[float, float, Dict[float, float]]: + """ + Runs prediction trials for models with varying tresholds to maximize the specified metric. + model (Union[str, Path]): Pipeline to evaluate. Can be a package or a path to a data directory. + data_path (Path): Path to file with DocBin with docs to use for threshold search. + pipe_name (str): Name of pipe to examine thresholds for. + threshold_key (str): Key of threshold attribute in component's configuration. + scores_key (str): Name of score to metric to optimize. + n_trials (int): Number of trials to determine optimal thresholds. + use_gpu (int): GPU ID or -1 for CPU. + gold_preproc (bool): Whether to use gold preprocessing. Gold preprocessing helps the annotations align to the + tokenization, and may result in sequences of more consistent length. However, it may reduce runtime accuracy due + to train/test skew. + silent (bool): Whether to print non-error-related output to stdout. + RETURNS (Tuple[float, float, Dict[float, float]]): Best found threshold, the corresponding score, scores for all + evaluated thresholds. + """ + + setup_gpu(use_gpu, silent=silent) + data_path = util.ensure_path(data_path) + if not data_path.exists(): + wasabi.msg.fail("Evaluation data not found", data_path, exits=1) + nlp = util.load_model(model) + + if pipe_name not in nlp.component_names: + raise AttributeError( + Errors.E001.format(name=pipe_name, opts=nlp.component_names) + ) + pipe = nlp.get_pipe(pipe_name) + if not hasattr(pipe, "scorer"): + raise AttributeError(Errors.E1045) + + if type(pipe) == TextCategorizer: + wasabi.msg.warn( + "The `textcat` component doesn't use a threshold as it's not applicable to the concept of " + "exclusive classes. All thresholds will yield the same results." + ) + + if not silent: + wasabi.msg.info( + title=f"Optimizing for {scores_key} for component '{pipe_name}' with {n_trials} " + f"trials." + ) + + # Load evaluation corpus. + corpus = Corpus(data_path, gold_preproc=gold_preproc) + dev_dataset = list(corpus(nlp)) + config_keys = threshold_key.split(".") + + def set_nested_item( + config: Dict[str, Any], keys: List[str], value: float + ) -> Dict[str, Any]: + """Set item in nested dictionary. Adapted from https://stackoverflow.com/a/54138200. + config (Dict[str, Any]): Configuration dictionary. + keys (List[Any]): Path to value to set. + value (float): Value to set. + RETURNS (Dict[str, Any]): Updated dictionary. + """ + functools.reduce(operator.getitem, keys[:-1], config)[keys[-1]] = value + return config + + def filter_config( + config: Dict[str, Any], keys: List[str], full_key: str + ) -> Dict[str, Any]: + """Filters provided config dictionary so that only the specified keys path remains. + config (Dict[str, Any]): Configuration dictionary. + keys (List[Any]): Path to value to set. + full_key (str): Full user-specified key. + RETURNS (Dict[str, Any]): Filtered dictionary. + """ + if keys[0] not in config: + wasabi.msg.fail( + title=f"Failed to look up `{full_key}` in config: sub-key {[keys[0]]} not found.", + text=f"Make sure you specified {[keys[0]]} correctly. The following sub-keys are available instead: " + f"{list(config.keys())}", + exits=1, + ) + return { + keys[0]: filter_config(config[keys[0]], keys[1:], full_key) + if len(keys) > 1 + else config[keys[0]] + } + + # Evaluate with varying threshold values. + scores: Dict[float, float] = {} + config_keys_full = ["components", pipe_name, *config_keys] + table_col_widths = (10, 10) + thresholds = numpy.linspace(0, 1, n_trials) + print(wasabi.tables.row(["Threshold", f"{scores_key}"], widths=table_col_widths)) + for threshold in thresholds: + # Reload pipeline with overrides specifying the new threshold. + nlp = util.load_model( + model, + config=set_nested_item( + filter_config( + nlp.config, config_keys_full, ".".join(config_keys_full) + ).copy(), + config_keys_full, + threshold, + ), + ) + if hasattr(pipe, "cfg"): + setattr( + nlp.get_pipe(pipe_name), + "cfg", + set_nested_item(getattr(pipe, "cfg"), config_keys, threshold), + ) + + eval_scores = nlp.evaluate(dev_dataset) + if scores_key not in eval_scores: + wasabi.msg.fail( + title=f"Failed to look up score `{scores_key}` in evaluation results.", + text=f"Make sure you specified the correct value for `scores_key`. The following scores are " + f"available: {list(eval_scores.keys())}", + exits=1, + ) + scores[threshold] = eval_scores[scores_key] + + if not isinstance(scores[threshold], (float, int)): + wasabi.msg.fail( + f"Returned score for key '{scores_key}' is not numeric. Threshold optimization only works for numeric " + f"scores.", + exits=1, + ) + print( + wasabi.row( + [round(threshold, 3), round(scores[threshold], 3)], + widths=table_col_widths, + ) + ) + + best_threshold = max(scores.keys(), key=(lambda key: scores[key])) + + # If all scores are identical, emit warning. + if len(set(scores.values())) == 1: + wasabi.msg.warn( + title="All scores are identical. Verify that all settings are correct.", + text="" + if ( + not isinstance(pipe, MultiLabel_TextCategorizer) + or scores_key in ("cats_macro_f", "cats_micro_f") + ) + else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`.", + ) + + else: + if not silent: + print( + f"\nBest threshold: {round(best_threshold, ndigits=4)} with {scores_key} value of {scores[best_threshold]}." + ) + + return best_threshold, scores[best_threshold], scores diff --git a/spacy/cli/project/assets.py b/spacy/cli/project/assets.py index 61438d1a8..8f35b2d23 100644 --- a/spacy/cli/project/assets.py +++ b/spacy/cli/project/assets.py @@ -189,7 +189,11 @@ def convert_asset_url(url: str) -> str: 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: + 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( diff --git a/spacy/cli/project/remote_storage.py b/spacy/cli/project/remote_storage.py index 336a4bcb3..076541580 100644 --- a/spacy/cli/project/remote_storage.py +++ b/spacy/cli/project/remote_storage.py @@ -5,14 +5,17 @@ import hashlib import urllib.parse import tarfile from pathlib import Path +from wasabi import msg -from .._util import get_hash, get_checksum, download_file, ensure_pathy -from ...util import make_tempdir, get_minor_version, ENV_VARS, check_bool_env_var +from .._util import get_hash, get_checksum, upload_file, download_file +from .._util import ensure_pathy, make_tempdir +from ...util import get_minor_version, ENV_VARS, check_bool_env_var from ...git_info import GIT_VERSION from ... import about +from ...errors import Errors if TYPE_CHECKING: - from pathy import Pathy # noqa: F401 + from pathy import FluidPath # noqa: F401 class RemoteStorage: @@ -27,7 +30,7 @@ class RemoteStorage: self.url = ensure_pathy(url) self.compression = compression - def push(self, path: Path, command_hash: str, content_hash: str) -> "Pathy": + 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. @@ -48,9 +51,7 @@ class RemoteStorage: 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)) - with tar_loc.open(mode="rb") as input_file: - with url.open(mode="wb") as output_file: - output_file.write(input_file.read()) + upload_file(tar_loc, url) return url def pull( @@ -59,7 +60,7 @@ class RemoteStorage: *, command_hash: Optional[str] = None, content_hash: Optional[str] = None, - ) -> Optional["Pathy"]: + ) -> Optional["FluidPath"]: """Retrieve a file from the remote cache. If the file already exists, nothing is done. @@ -84,7 +85,23 @@ class RemoteStorage: 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() - tar_file.extractall(self.root) + + # 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( @@ -93,25 +110,37 @@ class RemoteStorage: *, command_hash: Optional[str] = None, content_hash: Optional[str] = None, - ) -> Optional["Pathy"]: + ) -> 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.make_url(path, command_hash, content_hash) + url = self.url / name / command_hash / content_hash urls = [url] if url.exists() else [] elif command_hash is not None: - urls = list((self.url / name / command_hash).iterdir()) + if (self.url / name / command_hash).exists(): + urls = list((self.url / name / command_hash).iterdir()) else: - urls = list((self.url / name).iterdir()) - if content_hash is not None: - urls = [url for url in urls if url.parts[-1] == content_hash] + 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) -> "Pathy": + 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 diff --git a/spacy/cli/project/run.py b/spacy/cli/project/run.py index ebab7471e..a109c4a5a 100644 --- a/spacy/cli/project/run.py +++ b/spacy/cli/project/run.py @@ -53,6 +53,7 @@ def project_run( 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 @@ -69,6 +70,7 @@ def project_run( 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", [])} @@ -76,9 +78,10 @@ def project_run( validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand) req_path = project_dir / "requirements.txt" - if config.get("check_requirements", True) and os.path.exists(req_path): - with req_path.open() as requirements_file: - _check_requirements([req.replace("\n", "") for req in requirements_file]) + 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}'") @@ -90,6 +93,7 @@ def project_run( force=force, dry=dry, capture=capture, + skip_requirements_check=True, ) else: cmd = commands[subcommand] @@ -338,6 +342,12 @@ def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]: 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( diff --git a/spacy/cli/templates/quickstart_training.jinja b/spacy/cli/templates/quickstart_training.jinja index 58864883a..b961ac892 100644 --- a/spacy/cli/templates/quickstart_training.jinja +++ b/spacy/cli/templates/quickstart_training.jinja @@ -1,7 +1,7 @@ {# This is a template for training configs used for the quickstart widget in the docs and the init config command. It encodes various best practices and can help generate the best possible configuration, given a user's requirements. #} -{%- set use_transformer = hardware != "cpu" -%} +{%- set use_transformer = hardware != "cpu" and transformer_data -%} {%- set transformer = transformer_data[optimize] if use_transformer else {} -%} {%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "trainable_lemmatizer"] -%} [paths] diff --git a/spacy/cli/templates/quickstart_training_recommendations.yml b/spacy/cli/templates/quickstart_training_recommendations.yml index 27945e27a..4f214d22d 100644 --- a/spacy/cli/templates/quickstart_training_recommendations.yml +++ b/spacy/cli/templates/quickstart_training_recommendations.yml @@ -37,6 +37,15 @@ bn: accuracy: name: sagorsarker/bangla-bert-base size_factor: 3 +ca: + word_vectors: null + transformer: + efficiency: + name: projecte-aina/roberta-base-ca-v2 + size_factor: 3 + accuracy: + name: projecte-aina/roberta-base-ca-v2 + size_factor: 3 da: word_vectors: da_core_news_lg transformer: diff --git a/spacy/default_config.cfg b/spacy/default_config.cfg index 86a72926e..694fb732f 100644 --- a/spacy/default_config.cfg +++ b/spacy/default_config.cfg @@ -90,6 +90,8 @@ dev_corpus = "corpora.dev" train_corpus = "corpora.train" # Optional callback before nlp object is saved to disk after training before_to_disk = null +# Optional callback that is invoked at the start of each training step +before_update = null [training.logger] @loggers = "spacy.ConsoleLogger.v1" diff --git a/spacy/displacy/__init__.py b/spacy/displacy/__init__.py index 7bb300afa..bc32001d7 100644 --- a/spacy/displacy/__init__.py +++ b/spacy/displacy/__init__.py @@ -228,12 +228,13 @@ def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]: "kb_id": span.kb_id_ if span.kb_id_ else "", "kb_url": kb_url_template.format(span.kb_id_) if kb_url_template else "#", } - for span in doc.spans[spans_key] + for span in doc.spans.get(spans_key, []) ] tokens = [token.text for token in doc] if not spans: - warnings.warn(Warnings.W117.format(spans_key=spans_key)) + keys = list(doc.spans.keys()) + warnings.warn(Warnings.W117.format(spans_key=spans_key, keys=keys)) title = doc.user_data.get("title", None) if hasattr(doc, "user_data") else None settings = get_doc_settings(doc) return { diff --git a/spacy/errors.py b/spacy/errors.py index 820f7352e..82e7c52bc 100644 --- a/spacy/errors.py +++ b/spacy/errors.py @@ -199,7 +199,7 @@ class Warnings(metaclass=ErrorsWithCodes): W117 = ("No spans to visualize found in Doc object with spans_key: '{spans_key}'. If this is " "surprising to you, make sure the Doc was processed using a model " "that supports span categorization, and check the `doc.spans[spans_key]` " - "property manually if necessary.") + "property manually if necessary.\n\nAvailable keys: {keys}") W118 = ("Term '{term}' not found in glossary. It may however be explained in documentation " "for the corpora used to train the language. Please check " "`nlp.meta[\"sources\"]` for any relevant links.") @@ -212,8 +212,8 @@ class Warnings(metaclass=ErrorsWithCodes): W121 = ("Attempting to trace non-existent method '{method}' in pipe '{pipe}'") W122 = ("Couldn't trace method '{method}' in pipe '{pipe}'. This can happen if the pipe class " "is a Cython extension type.") - W123 = ("Argument {arg} with value {arg_value} is used instead of {config_value} as specified in the config. Be " - "aware that this might affect other components in your pipeline.") + W123 = ("Argument `enable` with value {enable} does not contain all values specified in the config option " + "`enabled` ({enabled}). Be aware that this might affect other components in your pipeline.") class Errors(metaclass=ErrorsWithCodes): @@ -544,6 +544,10 @@ class Errors(metaclass=ErrorsWithCodes): "during training, make sure to include it in 'annotating components'") # New errors added in v3.x + 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 " @@ -950,6 +954,7 @@ class Errors(metaclass=ErrorsWithCodes): "sure it's overwritten on the subclass.") E1046 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default " "knowledge base, use `InMemoryLookupKB`.") + E1047 = ("`find_threshold()` only supports components with a `scorer` attribute.") # v4 error strings E4000 = ("Expected a Doc as input, but got: '{type}'") diff --git a/spacy/lang/ru/lemmatizer.py b/spacy/lang/ru/lemmatizer.py index c37a3a91a..f4a35de38 100644 --- a/spacy/lang/ru/lemmatizer.py +++ b/spacy/lang/ru/lemmatizer.py @@ -28,34 +28,39 @@ class RussianLemmatizer(Lemmatizer): from pymorphy2 import MorphAnalyzer except ImportError: raise ImportError( - "The Russian lemmatizer mode 'pymorphy2' requires the " - "pymorphy2 library. Install it with: pip install pymorphy2" + "The lemmatizer mode 'pymorphy2' requires the " + "pymorphy2 library and dictionaries. Install them with: " + "pip install pymorphy2" + "# for Ukrainian dictionaries:" + "pip install pymorphy2-dicts-uk" ) from None if getattr(self, "_morph", None) is None: - self._morph = MorphAnalyzer() - elif mode == "pymorphy3": + self._morph = MorphAnalyzer(lang="ru") + elif mode in {"pymorphy3", "pymorphy3_lookup"}: try: from pymorphy3 import MorphAnalyzer except ImportError: raise ImportError( - "The Russian lemmatizer mode 'pymorphy3' requires the " - "pymorphy3 library. Install it with: pip install pymorphy3" + "The lemmatizer mode 'pymorphy3' requires the " + "pymorphy3 library and dictionaries. Install them with: " + "pip install pymorphy3" + "# for Ukrainian dictionaries:" + "pip install pymorphy3-dicts-uk" ) from None if getattr(self, "_morph", None) is None: - self._morph = MorphAnalyzer() + self._morph = MorphAnalyzer(lang="ru") super().__init__( vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer ) - def pymorphy2_lemmatize(self, token: Token) -> List[str]: + def _pymorphy_lemmatize(self, token: Token) -> List[str]: string = token.text univ_pos = token.pos_ morphology = token.morph.to_dict() if univ_pos == "PUNCT": return [PUNCT_RULES.get(string, string)] if univ_pos not in ("ADJ", "DET", "NOUN", "NUM", "PRON", "PROPN", "VERB"): - # Skip unchangeable pos - return [string.lower()] + return self._pymorphy_lookup_lemmatize(token) analyses = self._morph.parse(string) filtered_analyses = [] for analysis in analyses: @@ -63,8 +68,10 @@ class RussianLemmatizer(Lemmatizer): # Skip suggested parse variant for unknown word for pymorphy continue analysis_pos, _ = oc2ud(str(analysis.tag)) - if analysis_pos == univ_pos or ( - analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN") + if ( + analysis_pos == univ_pos + or (analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN")) + or ((analysis_pos == "PRON") and (univ_pos == "DET")) ): filtered_analyses.append(analysis) if not len(filtered_analyses): @@ -107,15 +114,27 @@ class RussianLemmatizer(Lemmatizer): dict.fromkeys([analysis.normal_form for analysis in filtered_analyses]) ) - def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]: + def _pymorphy_lookup_lemmatize(self, token: Token) -> List[str]: string = token.text analyses = self._morph.parse(string) - if len(analyses) == 1: - return [analyses[0].normal_form] + # often multiple forms would derive from the same normal form + # thus check _unique_ normal forms + normal_forms = set([an.normal_form for an in analyses]) + if len(normal_forms) == 1: + return [next(iter(normal_forms))] return [string] + def pymorphy2_lemmatize(self, token: Token) -> List[str]: + return self._pymorphy_lemmatize(token) + + def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]: + return self._pymorphy_lookup_lemmatize(token) + def pymorphy3_lemmatize(self, token: Token) -> List[str]: - return self.pymorphy2_lemmatize(token) + return self._pymorphy_lemmatize(token) + + def pymorphy3_lookup_lemmatize(self, token: Token) -> List[str]: + return self._pymorphy_lookup_lemmatize(token) def oc2ud(oc_tag: str) -> Tuple[str, Dict[str, str]]: diff --git a/spacy/lang/ru/tokenizer_exceptions.py b/spacy/lang/ru/tokenizer_exceptions.py index f3756e26c..e1889f785 100644 --- a/spacy/lang/ru/tokenizer_exceptions.py +++ b/spacy/lang/ru/tokenizer_exceptions.py @@ -61,6 +61,11 @@ for abbr in [ {ORTH: "2к23", NORM: "2023"}, {ORTH: "2к24", NORM: "2024"}, {ORTH: "2к25", NORM: "2025"}, + {ORTH: "2к26", NORM: "2026"}, + {ORTH: "2к27", NORM: "2027"}, + {ORTH: "2к28", NORM: "2028"}, + {ORTH: "2к29", NORM: "2029"}, + {ORTH: "2к30", NORM: "2030"}, ]: _exc[abbr[ORTH]] = [abbr] @@ -268,8 +273,8 @@ for abbr in [ {ORTH: "з-ка", NORM: "заимка"}, {ORTH: "п-к", NORM: "починок"}, {ORTH: "киш.", NORM: "кишлак"}, - {ORTH: "п. ст. ", NORM: "поселок станция"}, - {ORTH: "п. ж/д ст. ", NORM: "поселок при железнодорожной станции"}, + {ORTH: "п. ст.", NORM: "поселок станция"}, + {ORTH: "п. ж/д ст.", NORM: "поселок при железнодорожной станции"}, {ORTH: "ж/д бл-ст", NORM: "железнодорожный блокпост"}, {ORTH: "ж/д б-ка", NORM: "железнодорожная будка"}, {ORTH: "ж/д в-ка", NORM: "железнодорожная ветка"}, @@ -280,12 +285,12 @@ for abbr in [ {ORTH: "ж/д п.п.", NORM: "железнодорожный путевой пост"}, {ORTH: "ж/д о.п.", NORM: "железнодорожный остановочный пункт"}, {ORTH: "ж/д рзд.", NORM: "железнодорожный разъезд"}, - {ORTH: "ж/д ст. ", NORM: "железнодорожная станция"}, + {ORTH: "ж/д ст.", NORM: "железнодорожная станция"}, {ORTH: "м-ко", NORM: "местечко"}, {ORTH: "д.", NORM: "деревня"}, {ORTH: "с.", NORM: "село"}, {ORTH: "сл.", NORM: "слобода"}, - {ORTH: "ст. ", NORM: "станция"}, + {ORTH: "ст.", NORM: "станция"}, {ORTH: "ст-ца", NORM: "станица"}, {ORTH: "у.", NORM: "улус"}, {ORTH: "х.", NORM: "хутор"}, @@ -388,8 +393,9 @@ for abbr in [ {ORTH: "прим.", NORM: "примечание"}, {ORTH: "прим.ред.", NORM: "примечание редакции"}, {ORTH: "см. также", NORM: "смотри также"}, - {ORTH: "кв.м.", NORM: "квадрантный метр"}, - {ORTH: "м2", NORM: "квадрантный метр"}, + {ORTH: "см.", NORM: "смотри"}, + {ORTH: "кв.м.", NORM: "квадратный метр"}, + {ORTH: "м2", NORM: "квадратный метр"}, {ORTH: "б/у", NORM: "бывший в употреблении"}, {ORTH: "сокр.", NORM: "сокращение"}, {ORTH: "чел.", NORM: "человек"}, diff --git a/spacy/lang/uk/lemmatizer.py b/spacy/lang/uk/lemmatizer.py index 8337e7328..37015cc2a 100644 --- a/spacy/lang/uk/lemmatizer.py +++ b/spacy/lang/uk/lemmatizer.py @@ -29,7 +29,7 @@ class UkrainianLemmatizer(RussianLemmatizer): ) from None if getattr(self, "_morph", None) is None: self._morph = MorphAnalyzer(lang="uk") - elif mode == "pymorphy3": + elif mode in {"pymorphy3", "pymorphy3_lookup"}: try: from pymorphy3 import MorphAnalyzer except ImportError: diff --git a/spacy/language.py b/spacy/language.py index d391f15ab..e0abfd5e7 100644 --- a/spacy/language.py +++ b/spacy/language.py @@ -43,8 +43,7 @@ from .lookups import load_lookups from .compat import Literal -if TYPE_CHECKING: - from .pipeline import Pipe # noqa: F401 +PipeCallable = Callable[[Doc], Doc] # This is the base config will all settings (training etc.) @@ -181,7 +180,7 @@ class Language: self.vocab: Vocab = vocab if self.lang is None: self.lang = self.vocab.lang - self._components: List[Tuple[str, "Pipe"]] = [] + self._components: List[Tuple[str, PipeCallable]] = [] self._disabled: Set[str] = set() self.max_length = max_length # Create the default tokenizer from the default config @@ -303,7 +302,7 @@ class Language: return SimpleFrozenList(names) @property - def components(self) -> List[Tuple[str, "Pipe"]]: + def components(self) -> List[Tuple[str, PipeCallable]]: """Get all (name, component) tuples in the pipeline, including the currently disabled components. """ @@ -322,12 +321,12 @@ class Language: return SimpleFrozenList(names, error=Errors.E926.format(attr="component_names")) @property - def pipeline(self) -> List[Tuple[str, "Pipe"]]: + def pipeline(self) -> List[Tuple[str, PipeCallable]]: """The processing pipeline consisting of (name, component) tuples. The components are called on the Doc in order as it passes through the pipeline. - RETURNS (List[Tuple[str, Pipe]]): The pipeline. + RETURNS (List[Tuple[str, Callable[[Doc], Doc]]]): The pipeline. """ pipes = [(n, p) for n, p in self._components if n not in self._disabled] return SimpleFrozenList(pipes, error=Errors.E926.format(attr="pipeline")) @@ -527,7 +526,7 @@ class Language: assigns: Iterable[str] = SimpleFrozenList(), requires: Iterable[str] = SimpleFrozenList(), retokenizes: bool = False, - func: Optional["Pipe"] = None, + func: Optional[PipeCallable] = None, ) -> Callable[..., Any]: """Register a new pipeline component. Can be used for stateless function components that don't require a separate factory. Can be used as a @@ -542,7 +541,7 @@ class Language: e.g. "token.ent_id". Used for pipeline analysis. retokenizes (bool): Whether the component changes the tokenization. Used for pipeline analysis. - func (Optional[Callable]): Factory function if not used as a decorator. + func (Optional[Callable[[Doc], Doc]): Factory function if not used as a decorator. DOCS: https://spacy.io/api/language#component """ @@ -553,11 +552,11 @@ class Language: raise ValueError(Errors.E853.format(name=name)) component_name = name if name is not None else util.get_object_name(func) - def add_component(component_func: "Pipe") -> Callable: + def add_component(component_func: PipeCallable) -> Callable: if isinstance(func, type): # function is a class raise ValueError(Errors.E965.format(name=component_name)) - def factory_func(nlp, name: str) -> "Pipe": + def factory_func(nlp, name: str) -> PipeCallable: return component_func internal_name = cls.get_factory_name(name) @@ -607,7 +606,7 @@ class Language: print_pipe_analysis(analysis, keys=keys) return analysis - def get_pipe(self, name: str) -> "Pipe": + def get_pipe(self, name: str) -> PipeCallable: """Get a pipeline component for a given component name. name (str): Name of pipeline component to get. @@ -628,7 +627,7 @@ class Language: config: Dict[str, Any] = SimpleFrozenDict(), raw_config: Optional[Config] = None, validate: bool = True, - ) -> "Pipe": + ) -> PipeCallable: """Create a pipeline component. Mostly used internally. To create and add a component to the pipeline, you can use nlp.add_pipe. @@ -640,7 +639,7 @@ class Language: raw_config (Optional[Config]): Internals: the non-interpolated config. validate (bool): Whether to validate the component config against the arguments and types expected by the factory. - RETURNS (Pipe): The pipeline component. + RETURNS (Callable[[Doc], Doc]): The pipeline component. DOCS: https://spacy.io/api/language#create_pipe """ @@ -695,24 +694,18 @@ class Language: def create_pipe_from_source( self, source_name: str, source: "Language", *, name: str - ) -> Tuple["Pipe", str]: + ) -> Tuple[PipeCallable, str]: """Create a pipeline component by copying it from an existing model. source_name (str): Name of the component in the source pipeline. source (Language): The source nlp object to copy from. name (str): Optional alternative name to use in current pipeline. - RETURNS (Tuple[Callable, str]): The component and its factory name. + RETURNS (Tuple[Callable[[Doc], Doc], str]): The component and its factory name. """ # Check source type if not isinstance(source, Language): raise ValueError(Errors.E945.format(name=source_name, source=type(source))) - # Check vectors, with faster checks first - if ( - self.vocab.vectors.shape != source.vocab.vectors.shape - or self.vocab.vectors.key2row != source.vocab.vectors.key2row - or self.vocab.vectors.to_bytes(exclude=["strings"]) - != source.vocab.vectors.to_bytes(exclude=["strings"]) - ): + if self.vocab.vectors != source.vocab.vectors: warnings.warn(Warnings.W113.format(name=source_name)) if source_name not in source.component_names: raise KeyError( @@ -746,7 +739,7 @@ class Language: config: Dict[str, Any] = SimpleFrozenDict(), raw_config: Optional[Config] = None, validate: bool = True, - ) -> "Pipe": + ) -> PipeCallable: """Add a component to the processing pipeline. Valid components are callables that take a `Doc` object, modify it and return it. Only one of before/after/first/last can be set. Default behaviour is "last". @@ -769,7 +762,7 @@ class Language: raw_config (Optional[Config]): Internals: the non-interpolated config. validate (bool): Whether to validate the component config against the arguments and types expected by the factory. - RETURNS (Pipe): The pipeline component. + RETURNS (Callable[[Doc], Doc]): The pipeline component. DOCS: https://spacy.io/api/language#add_pipe """ @@ -790,14 +783,6 @@ class Language: factory_name, source, name=name ) else: - if not self.has_factory(factory_name): - err = Errors.E002.format( - name=factory_name, - opts=", ".join(self.factory_names), - method="add_pipe", - lang=util.get_object_name(self), - lang_code=self.lang, - ) pipe_component = self.create_pipe( factory_name, name=name, @@ -883,7 +868,7 @@ class Language: *, config: Dict[str, Any] = SimpleFrozenDict(), validate: bool = True, - ) -> "Pipe": + ) -> PipeCallable: """Replace a component in the pipeline. name (str): Name of the component to replace. @@ -892,7 +877,7 @@ class Language: component. Will be merged with default config, if available. validate (bool): Whether to validate the component config against the arguments and types expected by the factory. - RETURNS (Pipe): The new pipeline component. + RETURNS (Callable[[Doc], Doc]): The new pipeline component. DOCS: https://spacy.io/api/language#replace_pipe """ @@ -944,11 +929,11 @@ class Language: init_cfg = self._config["initialize"]["components"].pop(old_name) self._config["initialize"]["components"][new_name] = init_cfg - def remove_pipe(self, name: str) -> Tuple[str, "Pipe"]: + def remove_pipe(self, name: str) -> Tuple[str, PipeCallable]: """Remove a component from the pipeline. name (str): Name of the component to remove. - RETURNS (tuple): A `(name, component)` tuple of the removed component. + RETURNS (Tuple[str, Callable[[Doc], Doc]]): A `(name, component)` tuple of the removed component. DOCS: https://spacy.io/api/language#remove_pipe """ @@ -1363,15 +1348,15 @@ class Language: def set_error_handler( self, - error_handler: Callable[[str, "Pipe", List[Doc], Exception], NoReturn], + error_handler: Callable[[str, PipeCallable, List[Doc], Exception], NoReturn], ): - """Set an error handler object for all the components in the pipeline that implement - a set_error_handler function. + """Set an error handler object for all the components in the pipeline + that implement a set_error_handler function. - error_handler (Callable[[str, Pipe, List[Doc], Exception], NoReturn]): - Function that deals with a failing batch of documents. This callable function should take in - the component's name, the component itself, the offending batch of documents, and the exception - that was thrown. + error_handler (Callable[[str, Callable[[Doc], Doc], List[Doc], Exception], NoReturn]): + Function that deals with a failing batch of documents. This callable + function should take in the component's name, the component itself, + the offending batch of documents, and the exception that was thrown. DOCS: https://spacy.io/api/language#set_error_handler """ self.default_error_handler = error_handler @@ -1879,31 +1864,22 @@ class Language: if isinstance(exclude, str): exclude = [exclude] - def fetch_pipes_status(value: Iterable[str], key: str) -> Iterable[str]: - """Fetch value for `enable` or `disable` w.r.t. the specified config and passed arguments passed to - .load(). If both arguments and config specified values for this field, the passed arguments take precedence - and a warning is printed. - value (Iterable[str]): Passed value for `enable` or `disable`. - key (str): Key for field in config (either "enabled" or "disabled"). - RETURN (Iterable[str]): - """ - # We assume that no argument was passed if the value is the specified default value. - if id(value) == id(_DEFAULT_EMPTY_PIPES): - return config["nlp"].get(key, []) - else: - if len(config["nlp"].get(key, [])): - warnings.warn( - Warnings.W123.format( - arg=key[:-1], - arg_value=value, - config_value=config["nlp"][key], - ) + # `enable` should not be merged with `enabled` (the opposite is true for `disable`/`disabled`). If the config + # specifies values for `enabled` not included in `enable`, emit warning. + if id(enable) != id(_DEFAULT_EMPTY_PIPES): + enabled = config["nlp"].get("enabled", []) + if len(enabled) and not set(enabled).issubset(enable): + warnings.warn( + Warnings.W123.format( + enable=enable, + enabled=enabled, ) - return value + ) + # Ensure sets of disabled/enabled pipe names are not contradictory. disabled_pipes = cls._resolve_component_status( - fetch_pipes_status(disable, "disabled"), - fetch_pipes_status(enable, "enabled"), + list({*disable, *config["nlp"].get("disabled", [])}), + enable, config["nlp"]["pipeline"], ) nlp._disabled = set(p for p in disabled_pipes if p not in exclude) @@ -2084,10 +2060,12 @@ class Language: if enable: if isinstance(enable, str): enable = [enable] - to_disable = [ - pipe_name for pipe_name in pipe_names if pipe_name not in enable - ] - if disable and disable != to_disable: + to_disable = { + *[pipe_name for pipe_name in pipe_names if pipe_name not in enable], + *disable, + } + # If any pipe to be enabled is in to_disable, the specification is inconsistent. + if len(set(enable) & to_disable): raise ValueError(Errors.E1042.format(enable=enable, disable=disable)) return tuple(to_disable) diff --git a/spacy/matcher/matcher.pyx b/spacy/matcher/matcher.pyx index 8bd05f25f..bd139a55b 100644 --- a/spacy/matcher/matcher.pyx +++ b/spacy/matcher/matcher.pyx @@ -1,4 +1,4 @@ -# cython: infer_types=True, cython: profile=True +# cython: infer_types=True, profile=True from typing import List, Iterable from libcpp.vector cimport vector diff --git a/spacy/pipeline/spancat.py b/spacy/pipeline/spancat.py index 88f50d964..7a875dda9 100644 --- a/spacy/pipeline/spancat.py +++ b/spacy/pipeline/spancat.py @@ -2,7 +2,7 @@ from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast from typing import Union from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops from thinc.api import Optimizer -from thinc.types import Ragged, Ints2d, Floats2d, Ints1d +from thinc.types import Ragged, Ints2d, Floats2d import numpy @@ -282,7 +282,10 @@ class SpanCategorizer(TrainablePipe): DOCS: https://spacy.io/api/spancategorizer#predict """ indices = self.suggester(docs, ops=self.model.ops) - scores = self.model.predict((docs, indices)) # type: ignore + if indices.lengths.sum() == 0: + scores = self.model.ops.alloc2f(0, 0) + else: + scores = self.model.predict((docs, indices)) # type: ignore return {"indices": indices, "scores": scores} def set_candidates( diff --git a/spacy/pipeline/textcat.py b/spacy/pipeline/textcat.py index 9cebb9aeb..3c6732233 100644 --- a/spacy/pipeline/textcat.py +++ b/spacy/pipeline/textcat.py @@ -91,7 +91,6 @@ subword_features = true "cats_macro_f": None, "cats_macro_auc": None, "cats_f_per_type": None, - "cats_macro_auc_per_type": None, }, ) def make_textcat( @@ -169,7 +168,11 @@ class TextCategorizer(TrainablePipe): self.model = model self.name = name self._rehearsal_model = None - cfg: Dict[str, Any] = {"labels": [], "threshold": threshold, "positive_label": None} + cfg: Dict[str, Any] = { + "labels": [], + "threshold": threshold, + "positive_label": None, + } self.cfg = dict(cfg) self.scorer = scorer self.save_activations = save_activations @@ -416,5 +419,9 @@ class TextCategorizer(TrainablePipe): def _validate_categories(self, examples: Iterable[Example]): """Check whether the provided examples all have single-label cats annotations.""" for ex in examples: - if list(ex.reference.cats.values()).count(1.0) > 1: + vals = list(ex.reference.cats.values()) + if vals.count(1.0) > 1: raise ValueError(Errors.E895.format(value=ex.reference.cats)) + for val in vals: + if not (val == 1.0 or val == 0.0): + raise ValueError(Errors.E851.format(val=val)) diff --git a/spacy/pipeline/textcat_multilabel.py b/spacy/pipeline/textcat_multilabel.py index 3ba80653e..bdf933c10 100644 --- a/spacy/pipeline/textcat_multilabel.py +++ b/spacy/pipeline/textcat_multilabel.py @@ -88,7 +88,6 @@ subword_features = true "cats_macro_f": None, "cats_macro_auc": None, "cats_f_per_type": None, - "cats_macro_auc_per_type": None, }, ) def make_multilabel_textcat( @@ -205,6 +204,8 @@ class MultiLabel_TextCategorizer(TextCategorizer): for label in labels: self.add_label(label) subbatch = list(islice(get_examples(), 10)) + self._validate_categories(subbatch) + doc_sample = [eg.reference for eg in subbatch] label_sample, _ = self._examples_to_truth(subbatch) self._require_labels() @@ -215,4 +216,8 @@ class MultiLabel_TextCategorizer(TextCategorizer): def _validate_categories(self, examples: Iterable[Example]): """This component allows any type of single- or multi-label annotations. This method overwrites the more strict one from 'textcat'.""" - pass + # check that annotation values are valid + for ex in examples: + for val in ex.reference.cats.values(): + if not (val == 1.0 or val == 0.0): + raise ValueError(Errors.E851.format(val=val)) diff --git a/spacy/schemas.py b/spacy/schemas.py index 69ce3a396..dc30f9e39 100644 --- a/spacy/schemas.py +++ b/spacy/schemas.py @@ -329,6 +329,7 @@ class ConfigSchemaTraining(BaseModel): frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training") annotating_components: List[str] = Field(..., title="Pipeline components that should set annotations during training") before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk") + before_update: Optional[Callable[["Language", Dict[str, Any]], None]] = Field(..., title="Optional callback that is invoked at the start of each training step") # fmt: on class Config: diff --git a/spacy/tests/conftest.py b/spacy/tests/conftest.py index b1dc77ef0..2be286a57 100644 --- a/spacy/tests/conftest.py +++ b/spacy/tests/conftest.py @@ -351,17 +351,17 @@ def ru_tokenizer(): return get_lang_class("ru")().tokenizer -@pytest.fixture +@pytest.fixture(scope="session") def ru_lemmatizer(): pytest.importorskip("pymorphy3") return get_lang_class("ru")().add_pipe("lemmatizer") -@pytest.fixture +@pytest.fixture(scope="session") def ru_lookup_lemmatizer(): - pytest.importorskip("pymorphy2") + pytest.importorskip("pymorphy3") return get_lang_class("ru")().add_pipe( - "lemmatizer", config={"mode": "pymorphy2_lookup"} + "lemmatizer", config={"mode": "pymorphy3_lookup"} ) @@ -437,19 +437,19 @@ def uk_tokenizer(): return get_lang_class("uk")().tokenizer -@pytest.fixture +@pytest.fixture(scope="session") def uk_lemmatizer(): pytest.importorskip("pymorphy3") pytest.importorskip("pymorphy3_dicts_uk") return get_lang_class("uk")().add_pipe("lemmatizer") -@pytest.fixture +@pytest.fixture(scope="session") def uk_lookup_lemmatizer(): - pytest.importorskip("pymorphy2") - pytest.importorskip("pymorphy2_dicts_uk") + pytest.importorskip("pymorphy3") + pytest.importorskip("pymorphy3_dicts_uk") return get_lang_class("uk")().add_pipe( - "lemmatizer", config={"mode": "pymorphy2_lookup"} + "lemmatizer", config={"mode": "pymorphy3_lookup"} ) diff --git a/spacy/tests/doc/test_json_doc_conversion.py b/spacy/tests/doc/test_json_doc_conversion.py index 19698cfb2..11a1817e6 100644 --- a/spacy/tests/doc/test_json_doc_conversion.py +++ b/spacy/tests/doc/test_json_doc_conversion.py @@ -370,3 +370,12 @@ def test_json_to_doc_validation_error(doc): doc_json.pop("tokens") with pytest.raises(ValueError): Doc(doc.vocab).from_json(doc_json, validate=True) + + +def test_to_json_underscore_doc_getters(doc): + def get_text_length(doc): + return len(doc.text) + + Doc.set_extension("text_length", getter=get_text_length) + doc_json = doc.to_json(underscore=["text_length"]) + assert doc_json["_"]["text_length"] == get_text_length(doc) diff --git a/spacy/tests/lang/ru/test_lemmatizer.py b/spacy/tests/lang/ru/test_lemmatizer.py index e82fd4f8c..9a5a9ad68 100644 --- a/spacy/tests/lang/ru/test_lemmatizer.py +++ b/spacy/tests/lang/ru/test_lemmatizer.py @@ -81,6 +81,7 @@ def test_ru_lemmatizer_punct(ru_lemmatizer): def test_ru_doc_lookup_lemmatization(ru_lookup_lemmatizer): + assert ru_lookup_lemmatizer.mode == "pymorphy3_lookup" words = ["мама", "мыла", "раму"] pos = ["NOUN", "VERB", "NOUN"] morphs = [ @@ -92,3 +93,17 @@ def test_ru_doc_lookup_lemmatization(ru_lookup_lemmatizer): doc = ru_lookup_lemmatizer(doc) lemmas = [token.lemma_ for token in doc] assert lemmas == ["мама", "мыла", "раму"] + + +@pytest.mark.parametrize( + "word,lemma", + ( + ("бременем", "бремя"), + ("будешь", "быть"), + ("какая-то", "какой-то"), + ), +) +def test_ru_lookup_lemmatizer(ru_lookup_lemmatizer, word, lemma): + assert ru_lookup_lemmatizer.mode == "pymorphy3_lookup" + doc = Doc(ru_lookup_lemmatizer.vocab, words=[word]) + assert ru_lookup_lemmatizer(doc)[0].lemma_ == lemma diff --git a/spacy/tests/lang/uk/test_lemmatizer.py b/spacy/tests/lang/uk/test_lemmatizer.py index 788744aa1..a65bb25e5 100644 --- a/spacy/tests/lang/uk/test_lemmatizer.py +++ b/spacy/tests/lang/uk/test_lemmatizer.py @@ -8,12 +8,20 @@ pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning") def test_uk_lemmatizer(uk_lemmatizer): """Check that the default uk lemmatizer runs.""" doc = Doc(uk_lemmatizer.vocab, words=["a", "b", "c"]) + assert uk_lemmatizer.mode == "pymorphy3" uk_lemmatizer(doc) assert [token.lemma for token in doc] -def test_uk_lookup_lemmatizer(uk_lookup_lemmatizer): - """Check that the lookup uk lemmatizer runs.""" - doc = Doc(uk_lookup_lemmatizer.vocab, words=["a", "b", "c"]) - uk_lookup_lemmatizer(doc) - assert [token.lemma for token in doc] +@pytest.mark.parametrize( + "word,lemma", + ( + ("якийсь", "якийсь"), + ("розповідають", "розповідати"), + ("розповіси", "розповісти"), + ), +) +def test_uk_lookup_lemmatizer(uk_lookup_lemmatizer, word, lemma): + assert uk_lookup_lemmatizer.mode == "pymorphy3_lookup" + doc = Doc(uk_lookup_lemmatizer.vocab, words=[word]) + assert uk_lookup_lemmatizer(doc)[0].lemma_ == lemma diff --git a/spacy/tests/pipeline/test_pipe_methods.py b/spacy/tests/pipeline/test_pipe_methods.py index 14a7a36e5..4dd7bae16 100644 --- a/spacy/tests/pipeline/test_pipe_methods.py +++ b/spacy/tests/pipeline/test_pipe_methods.py @@ -615,20 +615,18 @@ def test_enable_disable_conflict_with_config(): with make_tempdir() as tmp_dir: nlp.to_disk(tmp_dir) - # Expected to fail, as config and arguments conflict. - with pytest.raises(ValueError): - spacy.load( - tmp_dir, enable=["tagger"], config={"nlp": {"disabled": ["senter"]}} - ) + # Expected to succeed, as config and arguments do not conflict. + assert spacy.load( + tmp_dir, enable=["tagger"], config={"nlp": {"disabled": ["senter"]}} + ).disabled == ["senter", "sentencizer"] # Expected to succeed without warning due to the lack of a conflicting config option. spacy.load(tmp_dir, enable=["tagger"]) - # Expected to succeed with a warning, as disable=[] should override the config setting. - with pytest.warns(UserWarning): + # Expected to fail due to conflict between enable and disabled. + with pytest.raises(ValueError): spacy.load( tmp_dir, - enable=["tagger"], - disable=[], - config={"nlp": {"disabled": ["senter"]}}, + enable=["senter"], + config={"nlp": {"disabled": ["senter", "tagger"]}}, ) diff --git a/spacy/tests/pipeline/test_spancat.py b/spacy/tests/pipeline/test_spancat.py index d7da6eb23..da9bffbc8 100644 --- a/spacy/tests/pipeline/test_spancat.py +++ b/spacy/tests/pipeline/test_spancat.py @@ -372,24 +372,39 @@ def test_overfitting_IO_overlapping(): def test_zero_suggestions(): - # Test with a suggester that returns 0 suggestions + # Test with a suggester that can return 0 suggestions - @registry.misc("test_zero_suggester") - def make_zero_suggester(): - def zero_suggester(docs, *, ops=None): + @registry.misc("test_mixed_zero_suggester") + def make_mixed_zero_suggester(): + def mixed_zero_suggester(docs, *, ops=None): if ops is None: ops = get_current_ops() - return Ragged( - ops.xp.zeros((0, 0), dtype="i"), ops.xp.zeros((len(docs),), dtype="i") - ) + spans = [] + lengths = [] + for doc in docs: + if len(doc) > 0 and len(doc) % 2 == 0: + spans.append((0, 1)) + lengths.append(1) + else: + lengths.append(0) + spans = ops.asarray2i(spans) + lengths_array = ops.asarray1i(lengths) + if len(spans) > 0: + output = Ragged(ops.xp.vstack(spans), lengths_array) + else: + output = Ragged(ops.xp.zeros((0, 0), dtype="i"), lengths_array) + return output - return zero_suggester + return mixed_zero_suggester fix_random_seed(0) nlp = English() spancat = nlp.add_pipe( "spancat", - config={"suggester": {"@misc": "test_zero_suggester"}, "spans_key": SPAN_KEY}, + config={ + "suggester": {"@misc": "test_mixed_zero_suggester"}, + "spans_key": SPAN_KEY, + }, ) train_examples = make_examples(nlp) optimizer = nlp.initialize(get_examples=lambda: train_examples) @@ -397,6 +412,16 @@ def test_zero_suggestions(): assert set(spancat.labels) == {"LOC", "PERSON"} nlp.update(train_examples, sgd=optimizer) + # empty doc + nlp("") + # single doc with zero suggestions + nlp("one") + # single doc with one suggestion + nlp("two two") + # batch with mixed zero/one suggestions + list(nlp.pipe(["one", "two two", "three three three", "", "four four four four"])) + # batch with no suggestions + list(nlp.pipe(["", "one", "three three three"])) def test_set_candidates(): diff --git a/spacy/tests/pipeline/test_textcat.py b/spacy/tests/pipeline/test_textcat.py index 46a0b15a7..931e7b322 100644 --- a/spacy/tests/pipeline/test_textcat.py +++ b/spacy/tests/pipeline/test_textcat.py @@ -361,6 +361,30 @@ def test_label_types(name): nlp.initialize() +@pytest.mark.parametrize( + "name,get_examples", + [ + ("textcat", make_get_examples_single_label), + ("textcat_multilabel", make_get_examples_multi_label), + ], +) +def test_invalid_label_value(name, get_examples): + nlp = Language() + textcat = nlp.add_pipe(name) + example_getter = get_examples(nlp) + + def invalid_examples(): + # make one example with an invalid score + examples = example_getter() + ref = examples[0].reference + key = list(ref.cats.keys())[0] + ref.cats[key] = 2.0 + return examples + + with pytest.raises(ValueError): + nlp.initialize(get_examples=invalid_examples) + + @pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"]) def test_no_label(name): nlp = Language() @@ -815,8 +839,8 @@ def test_textcat_loss(multi_label: bool, expected_loss: float): textcat = nlp.add_pipe("textcat_multilabel") else: textcat = nlp.add_pipe("textcat") - textcat.initialize(lambda: train_examples) assert isinstance(textcat, TextCategorizer) + textcat.initialize(lambda: train_examples) scores = textcat.model.ops.asarray( [[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype="f" # type: ignore ) diff --git a/spacy/tests/serialize/test_serialize_pipeline.py b/spacy/tests/serialize/test_serialize_pipeline.py index e49882441..36129a408 100644 --- a/spacy/tests/serialize/test_serialize_pipeline.py +++ b/spacy/tests/serialize/test_serialize_pipeline.py @@ -361,11 +361,10 @@ def test_serialize_pipeline_disable_enable(): assert nlp3.component_names == ["ner", "tagger"] with make_tempdir() as d: nlp3.to_disk(d) - with pytest.warns(UserWarning): - nlp4 = spacy.load(d, disable=["ner"]) - assert nlp4.pipe_names == ["tagger"] + nlp4 = spacy.load(d, disable=["ner"]) + assert nlp4.pipe_names == [] assert nlp4.component_names == ["ner", "tagger"] - assert nlp4.disabled == ["ner"] + assert nlp4.disabled == ["ner", "tagger"] with make_tempdir() as d: nlp.to_disk(d) nlp5 = spacy.load(d, exclude=["tagger"]) diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py index 838e00369..2e706458f 100644 --- a/spacy/tests/test_cli.py +++ b/spacy/tests/test_cli.py @@ -1,8 +1,11 @@ import os import math -from random import sample -from typing import Counter +from collections import Counter +from typing import Tuple, List, Dict, Any +import pkg_resources +import time +import numpy import pytest import srsly from click import NoSuchOption @@ -15,6 +18,7 @@ from spacy.cli._util import is_subpath_of, load_project_config from spacy.cli._util import parse_config_overrides, string_to_list from spacy.cli._util import substitute_project_variables from spacy.cli._util import validate_project_commands +from spacy.cli._util import upload_file, download_file from spacy.cli.debug_data import _compile_gold, _get_labels_from_model from spacy.cli.debug_data import _get_labels_from_spancat from spacy.cli.debug_data import _get_distribution, _get_kl_divergence @@ -25,12 +29,15 @@ from spacy.cli.download import get_compatibility, get_version from spacy.cli.init_config import RECOMMENDATIONS, init_config, fill_config from spacy.cli.package import get_third_party_dependencies from spacy.cli.package import _is_permitted_package_name +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.cli.find_threshold import find_threshold 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.tokens import Doc +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 spacy.training.converters import conll_ner_to_docs, conllu_to_docs @@ -589,6 +596,7 @@ def test_string_to_list_intify(value): assert string_to_list(value, intify=True) == [1, 2, 3] +@pytest.mark.skip(reason="Temporarily skip for dev version") def test_download_compatibility(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False @@ -599,6 +607,7 @@ def test_download_compatibility(): assert get_minor_version(about.__version__) == get_minor_version(version) +@pytest.mark.skip(reason="Temporarily skip for dev version") def test_validate_compatibility_table(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False @@ -855,3 +864,227 @@ def test_span_length_freq_dist_output_must_be_correct(): span_freqs = _get_spans_length_freq_dist(sample_span_lengths, threshold) assert sum(span_freqs.values()) >= threshold assert list(span_freqs.keys()) == [3, 1, 4, 5, 2] + + +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): + thresholds = numpy.linspace(0, 1, 10) + + def make_examples(nlp: Language) -> List[Example]: + docs: List[Example] = [] + + for t in [ + ( + "I am angry and confused in the Bank of America.", + { + "cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0}, + "spans": {"sc": [(31, 46, "ORG")]}, + }, + ), + ( + "I am confused but happy in New York.", + { + "cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0}, + "spans": {"sc": [(27, 35, "GPE")]}, + }, + ), + ]: + doc = nlp.make_doc(t[0]) + docs.append(Example.from_dict(doc, t[1])) + + return docs + + def init_nlp( + components: Tuple[Tuple[str, Dict[str, Any]], ...] = () + ) -> Tuple[Language, List[Example]]: + new_nlp = English() + new_nlp.add_pipe( # type: ignore + factory_name="textcat_multilabel", + name="tc_multi", + config={"threshold": 0.9}, + ) + + # Append additional components to pipeline. + for cfn, comp_config in components: + new_nlp.add_pipe(cfn, config=comp_config) + + new_examples = make_examples(new_nlp) + new_nlp.initialize(get_examples=lambda: new_examples) + for i in range(5): + new_nlp.update(new_examples) + + return new_nlp, new_examples + + with make_tempdir() as docs_dir: + # Check whether find_threshold() identifies lowest threshold above 0 as (first) ideal threshold, as this matches + # the current model behavior with the examples above. This can break once the model behavior changes and serves + # mostly as a smoke test. + nlp, examples = init_nlp() + DocBin(docs=[example.reference for example in examples]).to_disk( + docs_dir / "docs.spacy" + ) + with make_tempdir() as nlp_dir: + nlp.to_disk(nlp_dir) + res = find_threshold( + model=nlp_dir, + data_path=docs_dir / "docs.spacy", + pipe_name="tc_multi", + threshold_key="threshold", + scores_key="cats_macro_f", + silent=True, + ) + assert res[0] != thresholds[0] + assert thresholds[0] < res[0] < thresholds[9] + assert res[1] == 1.0 + assert res[2][1.0] == 0.0 + + # Test with spancat. + nlp, _ = init_nlp((("spancat", {}),)) + with make_tempdir() as nlp_dir: + nlp.to_disk(nlp_dir) + res = find_threshold( + model=nlp_dir, + data_path=docs_dir / "docs.spacy", + pipe_name="spancat", + threshold_key="threshold", + scores_key="spans_sc_f", + silent=True, + ) + assert res[0] != thresholds[0] + assert thresholds[0] < res[0] < thresholds[8] + assert res[1] >= 0.6 + assert res[2][1.0] == 0.0 + + # Having multiple textcat_multilabel components should work, since the name has to be specified. + nlp, _ = init_nlp((("textcat_multilabel", {}),)) + with make_tempdir() as nlp_dir: + nlp.to_disk(nlp_dir) + assert find_threshold( + model=nlp_dir, + data_path=docs_dir / "docs.spacy", + pipe_name="tc_multi", + threshold_key="threshold", + scores_key="cats_macro_f", + silent=True, + ) + + # Specifying the name of an non-existing pipe should fail. + nlp, _ = init_nlp() + with make_tempdir() as nlp_dir: + nlp.to_disk(nlp_dir) + with pytest.raises(AttributeError): + find_threshold( + model=nlp_dir, + data_path=docs_dir / "docs.spacy", + pipe_name="_", + threshold_key="threshold", + scores_key="cats_macro_f", + silent=True, + ) + + +@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): + # 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 diff --git a/spacy/tests/test_displacy.py b/spacy/tests/test_displacy.py index ccc145b44..f298b38e0 100644 --- a/spacy/tests/test_displacy.py +++ b/spacy/tests/test_displacy.py @@ -203,6 +203,16 @@ def test_displacy_parse_spans_different_spans_key(en_vocab): ] +def test_displacy_parse_empty_spans_key(en_vocab): + """Test that having an unset spans key doesn't raise an error""" + doc = Doc(en_vocab, words=["Welcome", "to", "the", "Bank", "of", "China"]) + doc.spans["custom"] = [Span(doc, 3, 6, "BANK")] + with pytest.warns(UserWarning, match="W117"): + spans = displacy.parse_spans(doc) + + assert isinstance(spans, dict) + + def test_displacy_parse_ents(en_vocab): """Test that named entities on a Doc are converted into displaCy's format.""" doc = Doc(en_vocab, words=["But", "Google", "is", "starting", "from", "behind"]) diff --git a/spacy/tests/training/test_training.py b/spacy/tests/training/test_training.py index 4384a796d..7933ea31f 100644 --- a/spacy/tests/training/test_training.py +++ b/spacy/tests/training/test_training.py @@ -2,6 +2,7 @@ import random import numpy import pytest +import spacy import srsly from spacy.lang.en import English from spacy.tokens import Doc, DocBin @@ -11,9 +12,10 @@ from spacy.training import offsets_to_biluo_tags from spacy.training.alignment_array import AlignmentArray from spacy.training.align import get_alignments from spacy.training.converters import json_to_docs +from spacy.training.loop import train_while_improving from spacy.util import get_words_and_spaces, load_model_from_path, minibatch from spacy.util import load_config_from_str -from thinc.api import compounding +from thinc.api import compounding, Adam from ..util import make_tempdir @@ -1112,3 +1114,39 @@ def test_retokenized_docs(doc): retokenizer.merge(doc1[0:2]) retokenizer.merge(doc1[5:7]) assert example.get_aligned("ORTH", as_string=True) == expected2 + + +def test_training_before_update(doc): + def before_update(nlp, args): + assert args["step"] == 0 + assert args["epoch"] == 1 + + # Raise an error here as the rest of the loop + # will not run to completion due to uninitialized + # models. + raise ValueError("ran_before_update") + + def generate_batch(): + yield 1, [Example(doc, doc)] + + nlp = spacy.blank("en") + nlp.add_pipe("tagger") + optimizer = Adam() + generator = train_while_improving( + nlp, + optimizer, + generate_batch(), + lambda: None, + dropout=0.1, + eval_frequency=100, + accumulate_gradient=10, + patience=10, + max_steps=100, + exclude=[], + annotating_components=[], + before_update=before_update, + ) + + with pytest.raises(ValueError, match="ran_before_update"): + for _ in generator: + pass diff --git a/spacy/tests/vocab_vectors/test_vectors.py b/spacy/tests/vocab_vectors/test_vectors.py index dd2cfc596..70835816d 100644 --- a/spacy/tests/vocab_vectors/test_vectors.py +++ b/spacy/tests/vocab_vectors/test_vectors.py @@ -626,3 +626,23 @@ def test_floret_vectors(floret_vectors_vec_str, floret_vectors_hashvec_str): OPS.to_numpy(vocab_r[word].vector), decimal=6, ) + + +def test_equality(): + vectors1 = Vectors(shape=(10, 10)) + vectors2 = Vectors(shape=(10, 8)) + + assert vectors1 != vectors2 + + vectors2 = Vectors(shape=(10, 10)) + assert vectors1 == vectors2 + + vectors1.add("hello", row=2) + assert vectors1 != vectors2 + + vectors2.add("hello", row=2) + assert vectors1 == vectors2 + + vectors1.resize((5, 9)) + vectors2.resize((5, 9)) + assert vectors1 == vectors2 diff --git a/spacy/tests/vocab_vectors/test_vocab_api.py b/spacy/tests/vocab_vectors/test_vocab_api.py index 16cf80a08..b9c386eb8 100644 --- a/spacy/tests/vocab_vectors/test_vocab_api.py +++ b/spacy/tests/vocab_vectors/test_vocab_api.py @@ -1,8 +1,13 @@ +import os + import pytest from spacy.attrs import IS_ALPHA, LEMMA, ORTH +from spacy.lang.en import English from spacy.parts_of_speech import NOUN, VERB from spacy.vocab import Vocab +from ..util import make_tempdir + @pytest.mark.issue(1868) def test_issue1868(): @@ -59,3 +64,19 @@ def test_vocab_api_contains(en_vocab, text): def test_vocab_writing_system(en_vocab): assert en_vocab.writing_system["direction"] == "ltr" assert en_vocab.writing_system["has_case"] is True + + +def test_to_disk(): + nlp = English() + with make_tempdir() as d: + nlp.vocab.to_disk(d) + assert "vectors" in os.listdir(d) + assert "lookups.bin" in os.listdir(d) + + +def test_to_disk_exclude(): + nlp = English() + with make_tempdir() as d: + nlp.vocab.to_disk(d, exclude=("vectors", "lookups")) + assert "vectors" not in os.listdir(d) + assert "lookups.bin" not in os.listdir(d) diff --git a/spacy/tokens/doc.pyx b/spacy/tokens/doc.pyx index ee6b6041c..bf3da0ce4 100644 --- a/spacy/tokens/doc.pyx +++ b/spacy/tokens/doc.pyx @@ -1674,6 +1674,20 @@ cdef class Doc: if underscore: user_keys = set() + # Handle doc attributes with .get to include values from getters + # and not only values stored in user_data, for backwards + # compatibility + for attr in underscore: + if self.has_extension(attr): + if "_" not in data: + data["_"] = {} + value = self._.get(attr) + if not srsly.is_json_serializable(value): + raise ValueError(Errors.E107.format(attr=attr, value=repr(value))) + data["_"][attr] = value + user_keys.add(attr) + # Token and span attributes only include values stored in user_data + # and not values generated by getters if self.user_data: for data_key, value in self.user_data.copy().items(): if type(data_key) == tuple and len(data_key) >= 4 and data_key[0] == "._.": @@ -1684,20 +1698,15 @@ cdef class Doc: user_keys.add(attr) if not srsly.is_json_serializable(value): raise ValueError(Errors.E107.format(attr=attr, value=repr(value))) - # Check if doc attribute - if start is None: - if "_" not in data: - data["_"] = {} - data["_"][attr] = value - # Check if token attribute - elif end is None: + # Token attribute + if start is not None and end is None: if "underscore_token" not in data: data["underscore_token"] = {} if attr not in data["underscore_token"]: data["underscore_token"][attr] = [] data["underscore_token"][attr].append({"start": start, "value": value}) - # Else span attribute - else: + # Span attribute + elif start is not None and end is not None: if "underscore_span" not in data: data["underscore_span"] = {} if attr not in data["underscore_span"]: diff --git a/spacy/training/loop.py b/spacy/training/loop.py index 06372cbb0..885257772 100644 --- a/spacy/training/loop.py +++ b/spacy/training/loop.py @@ -59,6 +59,7 @@ def train( batcher = T["batcher"] train_logger = T["logger"] before_to_disk = create_before_to_disk_callback(T["before_to_disk"]) + before_update = T["before_update"] # Helper function to save checkpoints. This is a closure for convenience, # to avoid passing in all the args all the time. @@ -89,6 +90,7 @@ def train( eval_frequency=T["eval_frequency"], exclude=frozen_components, annotating_components=annotating_components, + before_update=before_update, ) clean_output_dir(output_path) stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}") + "\n") @@ -150,6 +152,7 @@ def train_while_improving( max_steps: int, exclude: List[str], annotating_components: List[str], + before_update: Optional[Callable[["Language", Dict[str, Any]], None]], ): """Train until an evaluation stops improving. Works as a generator, with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`, @@ -198,6 +201,9 @@ def train_while_improving( words_seen = 0 start_time = timer() for step, (epoch, batch) in enumerate(train_data): + if before_update: + before_update_args = {"step": step, "epoch": epoch} + before_update(nlp, before_update_args) dropout = next(dropouts) # type: ignore for subbatch in subdivide_batch(batch, accumulate_gradient): nlp.update( diff --git a/spacy/util.py b/spacy/util.py index 809bc1814..4bdde1ad1 100644 --- a/spacy/util.py +++ b/spacy/util.py @@ -45,8 +45,7 @@ from . import about if TYPE_CHECKING: # This lets us add type hints for mypy etc. without causing circular imports - from .language import Language # noqa: F401 - from .pipeline import Pipe # noqa: F401 + from .language import Language, PipeCallable # noqa: F401 from .tokens import Doc, Span # noqa: F401 from .vocab import Vocab # noqa: F401 @@ -437,9 +436,9 @@ def load_model_from_package( name: str, *, vocab: Union["Vocab", bool] = True, - disable: Union[str, Iterable[str]] = SimpleFrozenList(), - enable: Union[str, Iterable[str]] = SimpleFrozenList(), - exclude: Union[str, Iterable[str]] = SimpleFrozenList(), + disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES, + enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES, + exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES, config: Union[Dict[str, Any], Config] = SimpleFrozenDict(), ) -> "Language": """Load a model from an installed package. @@ -613,9 +612,9 @@ def load_model_from_init_py( init_file: Union[Path, str], *, vocab: Union["Vocab", bool] = True, - disable: Union[str, Iterable[str]] = SimpleFrozenList(), - enable: Union[str, Iterable[str]] = SimpleFrozenList(), - exclude: Union[str, Iterable[str]] = SimpleFrozenList(), + disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES, + enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES, + exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES, config: Union[Dict[str, Any], Config] = SimpleFrozenDict(), ) -> "Language": """Helper function to use in the `load()` method of a model package's @@ -1636,9 +1635,11 @@ def check_bool_env_var(env_var: str) -> bool: def _pipe( docs: Iterable["Doc"], - proc: "Pipe", + proc: "PipeCallable", name: str, - default_error_handler: Callable[[str, "Pipe", List["Doc"], Exception], NoReturn], + default_error_handler: Callable[ + [str, "PipeCallable", List["Doc"], Exception], NoReturn + ], kwargs: Mapping[str, Any], ) -> Iterator["Doc"]: if hasattr(proc, "pipe"): diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx index 8300220c1..be0f6db09 100644 --- a/spacy/vectors.pyx +++ b/spacy/vectors.pyx @@ -243,6 +243,15 @@ cdef class Vectors: else: return key in self.key2row + 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"]) + ) + def resize(self, shape, inplace=False): """Resize the underlying vectors array. If inplace=True, the memory is reallocated. This may cause other references to the data to become diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx index d780dec0d..fc496a68b 100644 --- a/spacy/vocab.pyx +++ b/spacy/vocab.pyx @@ -467,9 +467,9 @@ cdef class Vocab: setters = ["strings", "vectors"] if "strings" not in exclude: self.strings.to_disk(path / "strings.json") - if "vectors" not in "exclude": + if "vectors" not in exclude: self.vectors.to_disk(path, exclude=["strings"]) - if "lookups" not in "exclude": + if "lookups" not in exclude: self.lookups.to_disk(path) def from_disk(self, path, *, exclude=tuple()): diff --git a/website/README.md b/website/README.md index db050cf03..890a48ef9 100644 --- a/website/README.md +++ b/website/README.md @@ -1,531 +1,11 @@ - - # spacy.io website and docs ![Netlify Status](https://api.netlify.com/api/v1/badges/d65fe97d-99ab-47f8-a339-1d8987251da0/deploy-status) -_This page contains the documentation and styleguide for the spaCy website. Its -rendered version is available at https://spacy.io/styleguide._ +The styleguide for the spaCy website is available at +[spacy.io/styleguide](https://spacy.io/styleguide). ---- - - - -The [spacy.io](https://spacy.io) website is implemented using -[Gatsby](https://www.gatsbyjs.org) with -[Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This -allows authoring content in **straightforward Markdown** without the usual -limitations. Standard elements can be overwritten with powerful -[React](http://reactjs.org/) components and wherever Markdown syntax isn't -enough, JSX components can be used. - -> #### Contributing to the site -> -> The docs can always use another example or more detail, and they should always -> be up to date and not misleading. We always appreciate a -> [pull request](https://github.com/explosion/spaCy/pulls). To quickly find the -> correct file to edit, simply click on the "Suggest edits" button at the bottom -> of a page. -> -> For more details on editing the site locally, see the installation -> instructions and markdown reference below. - -## Logo {#logo source="website/src/images/logo.svg"} - -import { Logos } from 'widgets/styleguide' - -If you would like to use the spaCy logo on your site, please get in touch and -ask us first. However, if you want to show support and tell others that your -project is using spaCy, you can grab one of our -[spaCy badges](/usage/spacy-101#faq-project-with-spacy). - - - -## Colors {#colors} - -import { Colors, Patterns } from 'widgets/styleguide' - - - -### Patterns - - - -## Typography {#typography} - -import { H1, H2, H3, H4, H5, Label, InlineList, Comment } from -'components/typography' - -> #### Markdown -> -> ```markdown_ -> ## Headline 2 -> ## Headline 2 {#some_id} -> ## Headline 2 {#some_id tag="method"} -> ``` -> -> #### JSX -> -> ```jsx ->

Headline 2

->

Headline 2

->

Headline 2

-> ``` - -Headlines are set in -[HK Grotesk](http://cargocollective.com/hanken/HK-Grotesk-Open-Source-Font) by -Hanken Design. All other body text and code uses the best-matching default -system font to provide a "native" reading experience. All code uses the -[JetBrains Mono](https://www.jetbrains.com/lp/mono/) typeface by JetBrains. - - - -Level 2 headings are automatically wrapped in `
` elements at compile -time, using a custom -[Markdown transformer](https://github.com/explosion/spaCy/tree/master/website/plugins/remark-wrap-section.js). -This makes it easier to highlight the section that's currently in the viewpoint -in the sidebar menu. - - - -
-

Headline 1

-

Headline 2

-

Headline 3

-

Headline 4

-
Headline 5
- -
- ---- - -The following optional attributes can be set on the headline to modify it. For -example, to add a tag for the documented type or mark features that have been -introduced in a specific version or require statistical models to be loaded. -Tags are also available as standalone `` components. - -| Argument | Example | Result | -| -------- | -------------------------- | ----------------------------------------- | -| `tag` | `{tag="method"}` | method | -| `new` | `{new="3"}` | 3 | -| `model` | `{model="tagger, parser"}` | tagger, parser | -| `hidden` | `{hidden="true"}` | | - -## Elements {#elements} - -### Links {#links} - -> #### Markdown -> -> ```markdown -> [I am a link](https://spacy.io) -> ``` -> -> #### JSX -> -> ```jsx -> I am a link -> ``` - -Special link styles are used depending on the link URL. - -- [I am a regular external link](https://explosion.ai) -- [I am a link to the documentation](/api/doc) -- [I am a link to an architecture](/api/architectures#HashEmbedCNN) -- [I am a link to a model](/models/en#en_core_web_sm) -- [I am a link to GitHub](https://github.com/explosion/spaCy) - -### Abbreviations {#abbr} - -import { Abbr } from 'components/typography' - -> #### JSX -> -> ```jsx -> Abbreviation -> ``` - -Some text with an abbreviation. On small -screens, I collapse and the explanation text is displayed next to the -abbreviation. - -### Tags {#tags} - -import Tag from 'components/tag' - -> ```jsx -> method -> 2.1 -> tagger, parser -> ``` - -Tags can be used together with headlines, or next to properties across the -documentation, and combined with tooltips to provide additional information. An -optional `variant` argument can be used for special tags. `variant="new"` makes -the tag take a version number to mark new features. Using the component, -visibility of this tag can later be toggled once the feature isn't considered -new anymore. Setting `variant="model"` takes a description of model capabilities -and can be used to mark features that require a respective model to be -installed. - - - -method 2 tagger, -parser - - - -### Buttons {#buttons} - -import Button from 'components/button' - -> ```jsx -> -> -> ``` - -Link buttons come in two variants, `primary` and `secondary` and two sizes, with -an optional `large` size modifier. Since they're mostly used as enhanced links, -the buttons are implemented as styled links instead of native button elements. - - - - -
- - - - -## Components - -### Table {#table} - -> #### Markdown -> -> ```markdown_ -> | Header 1 | Header 2 | -> | -------- | -------- | -> | Column 1 | Column 2 | -> ``` -> -> #### JSX -> -> ```markup -> -> -> ->
Header 1Header 2
Column 1Column 2
-> ``` - -Tables are used to present data and API documentation. Certain keywords can be -used to mark a footer row with a distinct style, for example to visualize the -return values of a documented function. - -| Header 1 | Header 2 | Header 3 | Header 4 | -| ----------- | -------- | :------: | -------: | -| Column 1 | Column 2 | Column 3 | Column 4 | -| Column 1 | Column 2 | Column 3 | Column 4 | -| Column 1 | Column 2 | Column 3 | Column 4 | -| Column 1 | Column 2 | Column 3 | Column 4 | -| **RETURNS** | Column 2 | Column 3 | Column 4 | - -Tables also support optional "divider" rows that are typically used to denote -keyword-only arguments in API documentation. To turn a row into a dividing -headline, it should only include content in its first cell, and its value should -be italicized: - -> #### Markdown -> -> ```markdown_ -> | Header 1 | Header 2 | Header 3 | -> | -------- | -------- | -------- | -> | Column 1 | Column 2 | Column 3 | -> | _Hello_ | | | -> | Column 1 | Column 2 | Column 3 | -> ``` - -| Header 1 | Header 2 | Header 3 | -| -------- | -------- | -------- | -| Column 1 | Column 2 | Column 3 | -| _Hello_ | | | -| Column 1 | Column 2 | Column 3 | - -### Type Annotations {#type-annotations} - -> #### Markdown -> -> ```markdown_ -> ~~Model[List[Doc], Floats2d]~~ -> ``` -> -> #### JSX -> -> ```markup -> Model[List[Doc], Floats2d] -> ``` - -Type annotations are special inline code blocks are used to describe Python -types in the [type hints](https://docs.python.org/3/library/typing.html) format. -The special component will split the type, apply syntax highlighting and link -all types that specify links in `meta/type-annotations.json`. Types can link to -internal or external documentation pages. To make it easy to represent the type -annotations in Markdown, the rendering "hijacks" the `~~` tags that would -typically be converted to a `` element – but in this case, text surrounded -by `~~` becomes a type annotation. - -- ~~Dict[str, List[Union[Doc, Span]]]~~ -- ~~Model[List[Doc], List[numpy.ndarray]]~~ - -Type annotations support a special visual style in tables and will render as a -separate row, under the cell text. This allows the API docs to display complex -types without taking up too much space in the cell. The type annotation should -always be the **last element** in the row. - -> #### Markdown -> -> ```markdown_ -> | Header 1 | Header 2 | -> | -------- | ----------------------- | -> | Column 1 | Column 2 ~~List[Doc]~~ | -> ``` - -| Name | Description | -| ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `vocab` | The shared vocabulary. ~~Vocab~~ | -| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. ~~Model[List[Doc], FullTransformerBatch]~~ | -| `set_extra_annotations` | Function that takes a batch of `Doc` objects and transformer outputs and can set additional annotations on the `Doc`. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ | - -### List {#list} - -> #### Markdown -> -> ```markdown_ -> 1. One -> 2. Two -> ``` -> -> #### JSX -> -> ```markup ->
    ->
  1. One
    2. ->
  2. Two
    3. ->
-> ``` - -Lists are available as bulleted and numbered. Markdown lists are transformed -automatically. - -- I am a bulleted list -- I have nice bullets -- Lorem ipsum dolor -- consectetur adipiscing elit - -1. I am an ordered list -2. I have nice numbers -3. Lorem ipsum dolor -4. consectetur adipiscing elit - -### Aside {#aside} - -> #### Markdown -> -> ```markdown_ -> > #### Aside title -> > This is aside text. -> ``` -> -> #### JSX -> -> ```jsx -> -> ``` - -Asides can be used to display additional notes and content in the right-hand -column. Asides can contain text, code and other elements if needed. Visually, -asides are moved to the side on the X-axis, and displayed at the same level they -were inserted. On small screens, they collapse and are rendered in their -original position, in between the text. - -To make them easier to use in Markdown, paragraphs formatted as blockquotes will -turn into asides by default. Level 4 headlines (with a leading `####`) will -become aside titles. - -### Code Block {#code-block} - -> #### Markdown -> -> ````markdown_ -> ```python -> ### This is a title -> import spacy -> ``` -> ```` -> -> #### JSX -> -> ```jsx -> -> import spacy -> -> ``` - -Code blocks use the [Prism](http://prismjs.com/) syntax highlighter with a -custom theme. The language can be set individually on each block, and defaults -to raw text with no highlighting. An optional label can be added as the first -line with the prefix `####` (Python-like) and `///` (JavaScript-like). the -indented block as plain text and preserve whitespace. - -```python -### Using spaCy -import spacy -nlp = spacy.load("en_core_web_sm") -doc = nlp("This is a sentence.") -for token in doc: - print(token.text, token.pos_) -``` - -Code blocks and also specify an optional range of line numbers to highlight by -adding `{highlight="..."}` to the headline. Acceptable ranges are spans like -`5-7`, but also `5-7,10` or `5-7,10,13-14`. - -> #### Markdown -> -> ````markdown_ -> ```python -> ### This is a title {highlight="1-2"} -> import spacy -> nlp = spacy.load("en_core_web_sm") -> ``` -> ```` - -```python -### Using the matcher {highlight="5-7"} -import spacy -from spacy.matcher import Matcher - -nlp = spacy.load('en_core_web_sm') -matcher = Matcher(nlp.vocab) -pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}] -matcher.add("HelloWorld", None, pattern) -doc = nlp("Hello, world! Hello world!") -matches = matcher(doc) -``` - -Adding `{executable="true"}` to the title turns the code into an executable -block, powered by [Binder](https://mybinder.org) and -[Juniper](https://github.com/ines/juniper). If JavaScript is disabled, the -interactive widget defaults to a regular code block. - -> #### Markdown -> -> ````markdown_ -> ```python -> ### {executable="true"} -> import spacy -> nlp = spacy.load("en_core_web_sm") -> ``` -> ```` - -```python -### {executable="true"} -import spacy -nlp = spacy.load("en_core_web_sm") -doc = nlp("This is a sentence.") -for token in doc: - print(token.text, token.pos_) -``` - -If a code block only contains a URL to a GitHub file, the raw file contents are -embedded automatically and syntax highlighting is applied. The link to the -original file is shown at the top of the widget. - -> #### Markdown -> -> ````markdown_ -> ```python -> https://github.com/... -> ``` -> ```` -> -> #### JSX -> -> ```jsx -> -> ``` - -```python -https://github.com/explosion/spaCy/tree/master/spacy/language.py -``` - -### Infobox {#infobox} - -import Infobox from 'components/infobox' - -> #### JSX -> -> ```jsx -> Regular infobox -> This is a warning. -> This is dangerous. -> ``` - -Infoboxes can be used to add notes, updates, warnings or additional information -to a page or section. Semantically, they're implemented and interpreted as an -`aside` element. Infoboxes can take an optional `title` argument, as well as an -optional `variant` (either `"warning"` or `"danger"`). - - - -If needed, an infobox can contain regular text, `inline code`, lists and other -blocks. - - - - - -If needed, an infobox can contain regular text, `inline code`, lists and other -blocks. - - - - - -If needed, an infobox can contain regular text, `inline code`, lists and other -blocks. - - - -### Accordion {#accordion} - -import Accordion from 'components/accordion' - -> #### JSX -> -> ```jsx -> -> Accordion content goes here. -> -> ``` - -Accordions are collapsible sections that are mostly used for lengthy tables, -like the tag and label annotation schemes for different languages. They all need -to be presented – but chances are the user doesn't actually care about _all_ of -them, especially not at the same time. So it's fairly reasonable to hide them -begin a click. This particular implementation was inspired by the amazing -[Inclusive Components blog](https://inclusive-components.design/collapsible-sections/). - - - -Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante, -pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt -nisi. Integer commodo pellentesque tincidunt. Nam at turpis finibus tortor -gravida sodales tincidunt sit amet est. Nullam euismod arcu in tortor auctor, -sit amet dignissim justo congue. - - - -## Setup and installation {#setup} +## Setup and installation Before running the setup, make sure your versions of [Node](https://nodejs.org/en/) and [npm](https://www.npmjs.com/) are up to date. @@ -554,14 +34,14 @@ extensions for your code editor. The [`.prettierrc`](https://github.com/explosion/spaCy/tree/master/website/.prettierrc) file in the root defines the settings used in this codebase. -## Building & developing the site with Docker {#docker} -Sometimes it's hard to get a local environment working due to rapid updates to node dependencies, -so it may be easier to use docker for building the docs. +## Building & developing the site with Docker -If you'd like to do this, -**be sure you do *not* include your local `node_modules` folder**, -since there are some dependencies that need to be built for the image system. -Rename it before using. +Sometimes it's hard to get a local environment working due to rapid updates to +node dependencies, so it may be easier to use docker for building the docs. + +If you'd like to do this, **be sure you do _not_ include your local +`node_modules` folder**, since there are some dependencies that need to be built +for the image system. Rename it before using. ```bash docker run -it \ @@ -571,16 +51,16 @@ docker run -it \ gatsby develop -H 0.0.0.0 ``` -This will allow you to access the built website at http://0.0.0.0:8000/ -in your browser, and still edit code in your editor while having the site -reflect those changes. +This will allow you to access the built website at http://0.0.0.0:8000/ in your +browser, and still edit code in your editor while having the site reflect those +changes. -**Note**: If you're working on a Mac with an M1 processor, -you might see segfault errors from `qemu` if you use the default image. -To fix this use the `arm64` tagged image in the `docker run` command +**Note**: If you're working on a Mac with an M1 processor, you might see +segfault errors from `qemu` if you use the default image. To fix this use the +`arm64` tagged image in the `docker run` command (ghcr.io/explosion/spacy-io:arm64). -### Building the Docker image {#docker-build} +### Building the Docker image If you'd like to build the image locally, you can do so like this: @@ -588,67 +68,21 @@ If you'd like to build the image locally, you can do so like this: docker build -t spacy-io . ``` -This will take some time, so if you want to use the prebuilt image you'll save a bit of time. +This will take some time, so if you want to use the prebuilt image you'll save a +bit of time. -## Markdown reference {#markdown} - -All page content and page meta lives in the `.md` files in the `/docs` -directory. The frontmatter block at the top of each file defines the page title -and other settings like the sidebar menu. - -````markdown ---- -title: Page title ---- - -## Headline starting a section {#some_id} - -This is a regular paragraph with a [link](https://spacy.io) and **bold text**. - -> #### This is an aside title -> -> This is aside text. - -### Subheadline - -| Header 1 | Header 2 | -| -------- | -------- | -| Column 1 | Column 2 | - -```python -### Code block title {highlight="2-3"} -import spacy -nlp = spacy.load("en_core_web_sm") -doc = nlp("Hello world") -``` - - - -This is content in the infobox. - - -```` - -In addition to the native markdown elements, you can use the components -[``][infobox], [``][accordion], [``][abbr] and -[``][tag] via their JSX syntax. - -[infobox]: https://spacy.io/styleguide#infobox -[accordion]: https://spacy.io/styleguide#accordion -[abbr]: https://spacy.io/styleguide#abbr -[tag]: https://spacy.io/styleguide#tag - -## Project structure {#structure} +## Project structure ```yaml -### Directory structure ├── docs # the actual markdown content ├── meta # JSON-formatted site metadata | ├── languages.json # supported languages and statistical models | ├── sidebars.json # sidebar navigations for different sections | ├── site.json # general site metadata +| ├── type-annotations.json # Type annotations | └── universe.json # data for the spaCy universe section ├── public # compiled site +├── setup # Jinja setup ├── src # source | ├── components # React components | ├── fonts # webfonts @@ -661,54 +95,10 @@ In addition to the native markdown elements, you can use the components | | ├── models.js # layout template for model pages | | └── universe.js # layout templates for universe | └── widgets # non-reusable components with content, e.g. changelog +├── .eslintrc.json # ESLint config file +├── .prettierrc # Prettier config file ├── gatsby-browser.js # browser-specific hooks for Gatsby ├── gatsby-config.js # Gatsby configuration ├── gatsby-node.js # Node-specific hooks for Gatsby └── package.json # package settings and dependencies ``` - -## Editorial {#editorial} - -- "spaCy" should always be spelled with a lowercase "s" and a capital "C", - unless it specifically refers to the Python package or Python import `spacy` - (in which case it should be formatted as code). - - ✅ spaCy is a library for advanced NLP in Python. - - ❌ Spacy is a library for advanced NLP in Python. - - ✅ First, you need to install the `spacy` package from pip. -- Mentions of code, like function names, classes, variable names etc. in inline - text should be formatted as `code`. - - ✅ "Calling the `nlp` object on a text returns a `Doc`." -- Objects that have pages in the [API docs](/api) should be linked – for - example, [`Doc`](/api/doc) or [`Language.to_disk`](/api/language#to_disk). The - mentions should still be formatted as code within the link. Links pointing to - the API docs will automatically receive a little icon. However, if a paragraph - includes many references to the API, the links can easily get messy. In that - case, we typically only link the first mention of an object and not any - subsequent ones. - - ✅ The [`Span`](/api/span) and [`Token`](/api/token) objects are views of a - [`Doc`](/api/doc). [`Span.as_doc`](/api/span#as_doc) creates a `Doc` object - from a `Span`. - - ❌ The [`Span`](/api/span) and [`Token`](/api/token) objects are views of a - [`Doc`](/api/doc). [`Span.as_doc`](/api/span#as_doc) creates a - [`Doc`](/api/doc) object from a [`Span`](/api/span). - -* Other things we format as code are: references to trained pipeline packages - like `en_core_web_sm` or file names like `code.py` or `meta.json`. - - - ✅ After training, the `config.cfg` is saved to disk. - -* [Type annotations](#type-annotations) are a special type of code formatting, - expressed by wrapping the text in `~~` instead of backticks. The result looks - like this: ~~List[Doc]~~. All references to known types will be linked - automatically. - - - ✅ The model has the input type ~~List[Doc]~~ and it outputs a - ~~List[Array2d]~~. - -* We try to keep links meaningful but short. - - ✅ For details, see the usage guide on - [training with custom code](/usage/training#custom-code). - - ❌ For details, see - [the usage guide on training with custom code](/usage/training#custom-code). - - ❌ For details, see the usage guide on training with custom code - [here](/usage/training#custom-code). diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md index fc2c46022..92a123241 100644 --- a/website/docs/api/cli.md +++ b/website/docs/api/cli.md @@ -12,10 +12,10 @@ menu: - ['train', 'train'] - ['pretrain', 'pretrain'] - ['evaluate', 'evaluate'] + - ['find-threshold', 'find-threshold'] - ['assemble', 'assemble'] - ['package', 'package'] - ['project', 'project'] - - ['ray', 'ray'] - ['huggingface-hub', 'huggingface-hub'] --- @@ -53,7 +53,7 @@ $ python -m spacy download [model] [--direct] [--sdist] [pip_args] | `--direct`, `-D` | Force direct download of exact package version. ~~bool (flag)~~ | | `--sdist`, `-S` 3 | Download the source package (`.tar.gz` archive) instead of the default pre-built binary wheel. ~~bool (flag)~~ | | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | -| pip args 2.1 | Additional installation options to be passed to `pip install` when installing the pipeline package. For example, `--user` to install to the user home directory or `--no-deps` to not install package dependencies. ~~Any (option/flag)~~ | +| pip args | Additional installation options to be passed to `pip install` when installing the pipeline package. For example, `--user` to install to the user home directory or `--no-deps` to not install package dependencies. ~~Any (option/flag)~~ | | **CREATES** | The installed pipeline package in your `site-packages` directory. | ## info {#info tag="command"} @@ -77,15 +77,15 @@ $ python -m spacy info [--markdown] [--silent] [--exclude] $ python -m spacy info [model] [--markdown] [--silent] [--exclude] ``` -| Name | Description | -| ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------- | -| `model` | A trained pipeline, i.e. package name or path (optional). ~~Optional[str] \(option)~~ | -| `--markdown`, `-md` | Print information as Markdown. ~~bool (flag)~~ | -| `--silent`, `-s` 2.0.12 | Don't print anything, just return the values. ~~bool (flag)~~ | -| `--exclude`, `-e` | Comma-separated keys to exclude from the print-out. Defaults to `"labels"`. ~~Optional[str]~~ | -| `--url`, `-u` 3.5.0 | Print the URL to download the most recent compatible version of the pipeline. Requires a pipeline name. ~~bool (flag)~~ | -| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | -| **PRINTS** | Information about your spaCy installation. | +| Name | Description | +| -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | +| `model` | A trained pipeline, i.e. package name or path (optional). ~~Optional[str] \(option)~~ | +| `--markdown`, `-md` | Print information as Markdown. ~~bool (flag)~~ | +| `--silent`, `-s` | Don't print anything, just return the values. ~~bool (flag)~~ | +| `--exclude`, `-e` | Comma-separated keys to exclude from the print-out. Defaults to `"labels"`. ~~Optional[str]~~ | +| `--url`, `-u` 3.5.0 | Print the URL to download the most recent compatible version of the pipeline. Requires a pipeline name. ~~bool (flag)~~ | +| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | +| **PRINTS** | Information about your spaCy installation. | ## validate {#validate new="2" tag="command"} @@ -260,22 +260,22 @@ chosen based on the file extension of the input file. $ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type] [--n-sents] [--seg-sents] [--base] [--morphology] [--merge-subtokens] [--ner-map] [--lang] ``` -| Name | Description | -| ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | -| `input_path` | Input file or directory. ~~Path (positional)~~ | -| `output_dir` | Output directory for converted file. Defaults to `"-"`, meaning data will be written to `stdout`. ~~Optional[Path] \(option)~~ | -| `--converter`, `-c` 2 | Name of converter to use (see below). ~~str (option)~~ | -| `--file-type`, `-t` 2.1 | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ | -| `--n-sents`, `-n` | Number of sentences per document. Supported for: `conll`, `conllu`, `iob`, `ner` ~~int (option)~~ | -| `--seg-sents`, `-s` 2.2 | Segment sentences. Supported for: `conll`, `ner` ~~bool (flag)~~ | -| `--base`, `-b`, `--model` | Trained spaCy pipeline for sentence segmentation to use as base (for `--seg-sents`). ~~Optional[str](option)~~ | -| `--morphology`, `-m` | Enable appending morphology to tags. Supported for: `conllu` ~~bool (flag)~~ | -| `--merge-subtokens`, `-T` | Merge CoNLL-U subtokens ~~bool (flag)~~ | -| `--ner-map`, `-nm` | NER tag mapping (as JSON-encoded dict of entity types). Supported for: `conllu` ~~Optional[Path](option)~~ | -| `--lang`, `-l` 2.1 | Language code (if tokenizer required). ~~Optional[str] \(option)~~ | -| `--concatenate`, `-C` | Concatenate output to a single file ~~bool (flag)~~ | -| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | -| **CREATES** | Binary [`DocBin`](/api/docbin) training data that can be used with [`spacy train`](/api/cli#train). | +| Name | Description | +| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | +| `input_path` | Input file or directory. ~~Path (positional)~~ | +| `output_dir` | Output directory for converted file. Defaults to `"-"`, meaning data will be written to `stdout`. ~~Optional[Path] \(option)~~ | +| `--converter`, `-c` | Name of converter to use (see below). ~~str (option)~~ | +| `--file-type`, `-t` | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ | +| `--n-sents`, `-n` | Number of sentences per document. Supported for: `conll`, `conllu`, `iob`, `ner` ~~int (option)~~ | +| `--seg-sents`, `-s` | Segment sentences. Supported for: `conll`, `ner` ~~bool (flag)~~ | +| `--base`, `-b`, `--model` | Trained spaCy pipeline for sentence segmentation to use as base (for `--seg-sents`). ~~Optional[str](option)~~ | +| `--morphology`, `-m` | Enable appending morphology to tags. Supported for: `conllu` ~~bool (flag)~~ | +| `--merge-subtokens`, `-T` | Merge CoNLL-U subtokens ~~bool (flag)~~ | +| `--ner-map`, `-nm` | NER tag mapping (as JSON-encoded dict of entity types). Supported for: `conllu` ~~Optional[Path](option)~~ | +| `--lang`, `-l` | Language code (if tokenizer required). ~~Optional[str] \(option)~~ | +| `--concatenate`, `-C` | Concatenate output to a single file ~~bool (flag)~~ | +| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | +| **CREATES** | Binary [`DocBin`](/api/docbin) training data that can be used with [`spacy train`](/api/cli#train). | ### Converters {#converters} @@ -474,8 +474,7 @@ report span characteristics such as the average span length and the span (or span boundary) distinctiveness. The distinctiveness measure shows how different the tokens are with respect to the rest of the corpus using the KL-divergence of the token distributions. To learn more, you can check out Papay et al.'s work on -[*Dissecting Span Identification Tasks with Performance Prediction* (EMNLP -2020)](https://aclanthology.org/2020.emnlp-main.396/). +[_Dissecting Span Identification Tasks with Performance Prediction_ (EMNLP 2020)](https://aclanthology.org/2020.emnlp-main.396/). @@ -1163,6 +1162,45 @@ $ python -m spacy evaluate [model] [data_path] [--output] [--code] [--gold-prepr | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | | **CREATES** | Training results and optional metrics and visualizations. | +## find-threshold {#find-threshold new="3.5" tag="command"} + +Runs prediction trials for a trained model with varying tresholds to maximize +the specified metric. The search space for the threshold is traversed linearly +from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout` +(the corresponding API call to `spacy.cli.find_threshold.find_threshold()` +returns all results). + +This is applicable only for components whose predictions are influenced by +thresholds - e.g. `textcat_multilabel` and `spancat`, but not `textcat`. Note +that the full path to the corresponding threshold attribute in the config has to +be provided. + +> #### Examples +> +> ```cli +> # For textcat_multilabel: +> $ python -m spacy find-threshold my_nlp data.spacy textcat_multilabel threshold cats_macro_f +> ``` +> +> ```cli +> # For spancat: +> $ python -m spacy find-threshold my_nlp data.spacy spancat threshold spans_sc_f +> ``` + +| Name | Description | +| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `model` | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~ | +| `data_path` | Path to file with DocBin with docs to use for threshold search. ~~Path (positional)~~ | +| `pipe_name` | Name of pipe to examine thresholds for. ~~str (positional)~~ | +| `threshold_key` | Key of threshold attribute in component's configuration. ~~str (positional)~~ | +| `scores_key` | Name of score to metric to optimize. ~~str (positional)~~ | +| `--n_trials`, `-n` | Number of trials to determine optimal thresholds. ~~int (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)~~ | +| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ | +| `--gold-preproc`, `-G` | Use gold preprocessing. ~~bool (flag)~~ | +| `--silent`, `-V`, `-VV` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ | +| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | + ## assemble {#assemble tag="command"} Assemble a pipeline from a config file without additional training. Expects a @@ -1229,19 +1267,19 @@ $ python -m spacy package [input_dir] [output_dir] [--code] [--meta-path] [--cre > $ pip install dist/en_pipeline-0.0.0.tar.gz > ``` -| Name | Description | -| ------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `input_dir` | Path to directory containing pipeline data. ~~Path (positional)~~ | -| `output_dir` | Directory to create package folder in. ~~Path (positional)~~ | -| `--code`, `-c` 3 | Comma-separated paths to Python files to be included in the package and imported in its `__init__.py`. This allows including [registering functions](/usage/training#custom-functions) and [custom components](/usage/processing-pipelines#custom-components). ~~str (option)~~ | -| `--meta-path`, `-m` 2 | Path to [`meta.json`](/api/data-formats#meta) file (optional). ~~Optional[Path] \(option)~~ | -| `--create-meta`, `-C` 2 | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. ~~bool (flag)~~ | -| `--build`, `-b` 3 | Comma-separated artifact formats to build. Can be `sdist` (for a `.tar.gz` archive) and/or `wheel` (for a binary `.whl` file), or `none` if you want to run this step manually. The generated artifacts can be installed by `pip install`. Defaults to `sdist`. ~~str (option)~~ | -| `--name`, `-n` 3 | Package name to override in meta. ~~Optional[str] \(option)~~ | -| `--version`, `-v` 3 | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. ~~Optional[str] \(option)~~ | -| `--force`, `-f` | Force overwriting of existing folder in output directory. ~~bool (flag)~~ | -| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | -| **CREATES** | A Python package containing the spaCy pipeline. | +| Name | Description | +| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `input_dir` | Path to directory containing pipeline data. ~~Path (positional)~~ | +| `output_dir` | Directory to create package folder in. ~~Path (positional)~~ | +| `--code`, `-c` 3 | Comma-separated paths to Python files to be included in the package and imported in its `__init__.py`. This allows including [registering functions](/usage/training#custom-functions) and [custom components](/usage/processing-pipelines#custom-components). ~~str (option)~~ | +| `--meta-path`, `-m` | Path to [`meta.json`](/api/data-formats#meta) file (optional). ~~Optional[Path] \(option)~~ | +| `--create-meta`, `-C` | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. ~~bool (flag)~~ | +| `--build`, `-b` 3 | Comma-separated artifact formats to build. Can be `sdist` (for a `.tar.gz` archive) and/or `wheel` (for a binary `.whl` file), or `none` if you want to run this step manually. The generated artifacts can be installed by `pip install`. Defaults to `sdist`. ~~str (option)~~ | +| `--name`, `-n` 3 | Package name to override in meta. ~~Optional[str] \(option)~~ | +| `--version`, `-v` 3 | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. ~~Optional[str] \(option)~~ | +| `--force`, `-f` | Force overwriting of existing folder in output directory. ~~bool (flag)~~ | +| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | +| **CREATES** | A Python package containing the spaCy pipeline. | ## project {#project new="3"} @@ -1352,12 +1390,13 @@ If the contents are different, the new version of the file is uploaded. Deleting obsolete files is left up to you. Remotes can be defined in the `remotes` section of the -[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses the -[`smart-open`](https://github.com/RaRe-Technologies/smart_open) library to -communicate with the remote storages, so you can use any protocol that -`smart-open` supports, including [S3](https://aws.amazon.com/s3/), -[Google Cloud Storage](https://cloud.google.com/storage), SSH and more, although -you may need to install extra dependencies to use certain protocols. +[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses +[`Pathy`](https://github.com/justindujardin/pathy) to communicate with the +remote storages, so you can use any protocol that `Pathy` supports, including +[S3](https://aws.amazon.com/s3/), +[Google Cloud Storage](https://cloud.google.com/storage), and the local +filesystem, although you may need to install extra dependencies to use certain +protocols. ```cli $ python -m spacy project push [remote] [project_dir] @@ -1396,12 +1435,13 @@ outputs, so if you change the config back, you'll be able to fetch back the result. Remotes can be defined in the `remotes` section of the -[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses the -[`smart-open`](https://github.com/RaRe-Technologies/smart_open) library to -communicate with the remote storages, so you can use any protocol that -`smart-open` supports, including [S3](https://aws.amazon.com/s3/), -[Google Cloud Storage](https://cloud.google.com/storage), SSH and more, although -you may need to install extra dependencies to use certain protocols. +[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses +[`Pathy`](https://github.com/justindujardin/pathy) to communicate with the +remote storages, so you can use any protocol that `Pathy` supports, including +[S3](https://aws.amazon.com/s3/), +[Google Cloud Storage](https://cloud.google.com/storage), and the local +filesystem, although you may need to install extra dependencies to use certain +protocols. ```cli $ python -m spacy project pull [remote] [project_dir] @@ -1503,50 +1543,6 @@ $ python -m spacy project dvc [project_dir] [workflow] [--force] [--verbose] [-- | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | | **CREATES** | A `dvc.yaml` file in the project directory, based on the steps defined in the given workflow. | -## ray {#ray new="3"} - -The `spacy ray` CLI includes commands for parallel and distributed computing via -[Ray](https://ray.io). - - - -To use this command, you need the -[`spacy-ray`](https://github.com/explosion/spacy-ray) package installed. -Installing the package will automatically add the `ray` command to the spaCy -CLI. - - - -### ray train {#ray-train tag="command"} - -Train a spaCy pipeline using [Ray](https://ray.io) for parallel training. The -command works just like [`spacy train`](/api/cli#train). For more details and -examples, see the usage guide on -[parallel training](/usage/training#parallel-training) and the spaCy project -[integration](/usage/projects#ray). - -```cli -$ python -m spacy ray train [config_path] [--code] [--output] [--n-workers] [--address] [--gpu-id] [--verbose] [overrides] -``` - -> #### Example -> -> ```cli -> $ python -m spacy ray train config.cfg --n-workers 2 -> ``` - -| Name | Description | -| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (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)~~ | -| `--output`, `-o` | Directory or remote storage URL for saving trained pipeline. The directory will be created if it doesn't exist. ~~Optional[Path] \(option)~~ | -| `--n-workers`, `-n` | The number of workers. Defaults to `1`. ~~int (option)~~ | -| `--address`, `-a` | Optional address of the Ray cluster. If not set (default), Ray will run locally. ~~Optional[str] \(option)~~ | -| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ | -| `--verbose`, `-V` | Display more information for debugging purposes. ~~bool (flag)~~ | -| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | -| 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)~~ | - ## huggingface-hub {#huggingface-hub new="3.1"} The `spacy huggingface-cli` CLI includes commands for uploading your trained diff --git a/website/docs/api/data-formats.md b/website/docs/api/data-formats.md index ce06c4ea8..768844cf3 100644 --- a/website/docs/api/data-formats.md +++ b/website/docs/api/data-formats.md @@ -186,6 +186,7 @@ process that are used when you run [`spacy train`](/api/cli#train). | `accumulate_gradient` | Whether to divide the batch up into substeps. Defaults to `1`. ~~int~~ | | `batcher` | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. Defaults to [`batch_by_words`](/api/top-level#batch_by_words). ~~Callable[[Iterator[Doc], Iterator[List[Doc]]]]~~ | | `before_to_disk` | Optional callback to modify `nlp` object right before it is saved to disk during and after training. Can be used to remove or reset config values or disable components. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ | +| `before_update` | Optional callback that is invoked at the start of each training step with the `nlp` object and a `Dict` containing the following entries: `step`, `epoch`. Can be used to make deferred changes to components. Defaults to `null`. ~~Optional[Callable[[Language, Dict[str, Any]], None]]~~ | | `dev_corpus` | Dot notation of the config location defining the dev corpus. Defaults to `corpora.dev`. ~~str~~ | | `dropout` | The dropout rate. Defaults to `0.1`. ~~float~~ | | `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ | diff --git a/website/docs/api/doc.md b/website/docs/api/doc.md index 433134278..235470934 100644 --- a/website/docs/api/doc.md +++ b/website/docs/api/doc.md @@ -209,15 +209,15 @@ alignment mode `"strict". > assert span.text == "New York" > ``` -| Name | Description | -| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `start` | The index of the first character of the span. ~~int~~ | -| `end` | The index of the last character after the span. ~~int~~ | -| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ | -| `kb_id` 2.2 | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ | -| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ | -| `alignment_mode` | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ | -| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ | +| Name | Description | +| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `start` | The index of the first character of the span. ~~int~~ | +| `end` | The index of the last character after the span. ~~int~~ | +| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ | +| `kb_id` | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ | +| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ | +| `alignment_mode` | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ | +| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ | ## Doc.set_ents {#set_ents tag="method" new="3"} @@ -757,10 +757,10 @@ The L2 norm of the document's vector representation. | `text_with_ws` | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. ~~str~~ | | `mem` | The document's local memory heap, for all C data it owns. ~~cymem.Pool~~ | | `vocab` | The store of lexical types. ~~Vocab~~ | -| `tensor` 2 | Container for dense vector representations. ~~numpy.ndarray~~ | +| `tensor` | Container for dense vector representations. ~~numpy.ndarray~~ | | `user_data` | A generic storage area, for user custom data. ~~Dict[str, Any]~~ | -| `lang` 2.1 | Language of the document's vocabulary. ~~int~~ | -| `lang_` 2.1 | Language of the document's vocabulary. ~~str~~ | +| `lang` | Language of the document's vocabulary. ~~int~~ | +| `lang_` | Language of the document's vocabulary. ~~str~~ | | `user_hooks` | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~ | | `user_token_hooks` | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~ | | `user_span_hooks` | A dictionary that allows customization of properties of `Span` children. ~~Dict[str, Callable]~~ | diff --git a/website/docs/api/language.md b/website/docs/api/language.md index 767a7450a..ad0ac2a46 100644 --- a/website/docs/api/language.md +++ b/website/docs/api/language.md @@ -63,18 +63,18 @@ spaCy loads a model under the hood based on its > nlp = Language.from_config(config) > ``` -| Name | Description | -| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `config` | The loaded config. ~~Union[Dict[str, Any], Config]~~ | -| _keyword-only_ | | -| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ | -| `disable` | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [`nlp.enable_pipe`](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ | -| `enable` 3.4 | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled, but can be enabled again using [`nlp.enable_pipe`](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ | -| `exclude` | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~ | -| `meta` | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~ | -| `auto_fill` | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. ~~bool~~ | -| `validate` | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ | -| **RETURNS** | The initialized object. ~~Language~~ | +| Name | Description | +| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `config` | The loaded config. ~~Union[Dict[str, Any], Config]~~ | +| _keyword-only_ | | +| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ | +| `disable` | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). Is merged with the config entry `nlp.disabled`. ~~Union[str, Iterable[str]]~~ | +| `enable` 3.4 | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled, but can be enabled again using [nlp.enable_pipe](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ | +| `exclude` | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~ | +| `meta` | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~ | +| `auto_fill` | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. ~~bool~~ | +| `validate` | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ | +| **RETURNS** | The initialized object. ~~Language~~ | ## Language.component {#component tag="classmethod" new="3"} @@ -198,16 +198,16 @@ tokenization is skipped but the rest of the pipeline is run. > assert doc.has_annotation("DEP") > ``` -| Name | Description | -| ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `texts` | A sequence of strings (or `Doc` objects). ~~Iterable[Union[str, Doc]]~~ | -| _keyword-only_ | | -| `as_tuples` | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. ~~bool~~ | -| `batch_size` | The number of texts to buffer. ~~Optional[int]~~ | -| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ | -| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ | -| `n_process` 2.2.2 | Number of processors to use. Defaults to `1`. ~~int~~ | -| **YIELDS** | Documents in the order of the original text. ~~Doc~~ | +| Name | Description | +| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `texts` | A sequence of strings (or `Doc` objects). ~~Iterable[Union[str, Doc]]~~ | +| _keyword-only_ | | +| `as_tuples` | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. ~~bool~~ | +| `batch_size` | The number of texts to buffer. ~~Optional[int]~~ | +| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ | +| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ | +| `n_process` | Number of processors to use. Defaults to `1`. ~~int~~ | +| **YIELDS** | Documents in the order of the original text. ~~Doc~~ | ## Language.set_error_handler {#set_error_handler tag="method" new="3"} @@ -1030,21 +1030,21 @@ details. ## Attributes {#attributes} -| Name | Description | -| --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | -| `vocab` | A container for the lexical types. ~~Vocab~~ | -| `tokenizer` | The tokenizer. ~~Tokenizer~~ | -| `make_doc` | Callable that takes a string and returns a `Doc`. ~~Callable[[str], Doc]~~ | -| `pipeline` | List of `(name, component)` tuples describing the current processing pipeline, in order. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ | -| `pipe_names` 2 | List of pipeline component names, in order. ~~List[str]~~ | -| `pipe_labels` 2.2 | List of labels set by the pipeline components, if available, keyed by component name. ~~Dict[str, List[str]]~~ | -| `pipe_factories` 2.2 | Dictionary of pipeline component names, mapped to their factory names. ~~Dict[str, str]~~ | -| `factories` | All available factory functions, keyed by name. ~~Dict[str, Callable[[...], Callable[[Doc], Doc]]]~~ | -| `factory_names` 3 | List of all available factory names. ~~List[str]~~ | -| `components` 3 | List of all available `(name, component)` tuples, including components that are currently disabled. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ | -| `component_names` 3 | List of all available component names, including components that are currently disabled. ~~List[str]~~ | -| `disabled` 3 | Names of components that are currently disabled and don't run as part of the pipeline. ~~List[str]~~ | -| `path` 2 | Path to the pipeline data directory, if a pipeline is loaded from a path or package. Otherwise `None`. ~~Optional[Path]~~ | +| Name | Description | +| -------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | +| `vocab` | A container for the lexical types. ~~Vocab~~ | +| `tokenizer` | The tokenizer. ~~Tokenizer~~ | +| `make_doc` | Callable that takes a string and returns a `Doc`. ~~Callable[[str], Doc]~~ | +| `pipeline` | List of `(name, component)` tuples describing the current processing pipeline, in order. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ | +| `pipe_names` | List of pipeline component names, in order. ~~List[str]~~ | +| `pipe_labels` | List of labels set by the pipeline components, if available, keyed by component name. ~~Dict[str, List[str]]~~ | +| `pipe_factories` | Dictionary of pipeline component names, mapped to their factory names. ~~Dict[str, str]~~ | +| `factories` | All available factory functions, keyed by name. ~~Dict[str, Callable[[...], Callable[[Doc], Doc]]]~~ | +| `factory_names` 3 | List of all available factory names. ~~List[str]~~ | +| `components` 3 | List of all available `(name, component)` tuples, including components that are currently disabled. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ | +| `component_names` 3 | List of all available component names, including components that are currently disabled. ~~List[str]~~ | +| `disabled` 3 | Names of components that are currently disabled and don't run as part of the pipeline. ~~List[str]~~ | +| `path` | Path to the pipeline data directory, if a pipeline is loaded from a path or package. Otherwise `None`. ~~Optional[Path]~~ | ## Class attributes {#class-attributes} diff --git a/website/docs/api/lexeme.md b/website/docs/api/lexeme.md index db1aba7aa..cd4086562 100644 --- a/website/docs/api/lexeme.md +++ b/website/docs/api/lexeme.md @@ -121,43 +121,43 @@ The L2 norm of the lexeme's vector representation. ## Attributes {#attributes} -| Name | Description | -| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `vocab` | The lexeme's vocabulary. ~~Vocab~~ | -| `text` | Verbatim text content. ~~str~~ | -| `orth` | ID of the verbatim text content. ~~int~~ | -| `orth_` | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. ~~str~~ | -| `rank` | Sequential ID of the lexeme's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | -| `flags` | Container of the lexeme's binary flags. ~~int~~ | -| `norm` | The lexeme's norm, i.e. a normalized form of the lexeme text. ~~int~~ | -| `norm_` | The lexeme's norm, i.e. a normalized form of the lexeme text. ~~str~~ | -| `lower` | Lowercase form of the word. ~~int~~ | -| `lower_` | Lowercase form of the word. ~~str~~ | -| `shape` | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~int~~ | -| `shape_` | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~str~~ | -| `prefix` | Length-N substring from the start of the word. Defaults to `N=1`. ~~int~~ | -| `prefix_` | Length-N substring from the start of the word. Defaults to `N=1`. ~~str~~ | -| `suffix` | Length-N substring from the end of the word. Defaults to `N=3`. ~~int~~ | -| `suffix_` | Length-N substring from the start of the word. Defaults to `N=3`. ~~str~~ | -| `is_alpha` | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. ~~bool~~ | -| `is_ascii` | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. ~~bool~~ | -| `is_digit` | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. ~~bool~~ | -| `is_lower` | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. ~~bool~~ | -| `is_upper` | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. ~~bool~~ | -| `is_title` | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. ~~bool~~ | -| `is_punct` | Is the lexeme punctuation? ~~bool~~ | -| `is_left_punct` | Is the lexeme a left punctuation mark, e.g. `(`? ~~bool~~ | -| `is_right_punct` | Is the lexeme a right punctuation mark, e.g. `)`? ~~bool~~ | -| `is_space` | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. ~~bool~~ | -| `is_bracket` | Is the lexeme a bracket? ~~bool~~ | -| `is_quote` | Is the lexeme a quotation mark? ~~bool~~ | -| `is_currency` 2.0.8 | Is the lexeme a currency symbol? ~~bool~~ | -| `like_url` | Does the lexeme resemble a URL? ~~bool~~ | -| `like_num` | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ | -| `like_email` | Does the lexeme resemble an email address? ~~bool~~ | -| `is_oov` | Is the lexeme out-of-vocabulary (i.e. does it not have a word vector)? ~~bool~~ | -| `is_stop` | Is the lexeme part of a "stop list"? ~~bool~~ | -| `lang` | Language of the parent vocabulary. ~~int~~ | -| `lang_` | Language of the parent vocabulary. ~~str~~ | -| `prob` | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). ~~float~~ | -| `cluster` | Brown cluster ID. ~~int~~ | +| Name | Description | +| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `vocab` | The lexeme's vocabulary. ~~Vocab~~ | +| `text` | Verbatim text content. ~~str~~ | +| `orth` | ID of the verbatim text content. ~~int~~ | +| `orth_` | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. ~~str~~ | +| `rank` | Sequential ID of the lexeme's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | +| `flags` | Container of the lexeme's binary flags. ~~int~~ | +| `norm` | The lexeme's norm, i.e. a normalized form of the lexeme text. ~~int~~ | +| `norm_` | The lexeme's norm, i.e. a normalized form of the lexeme text. ~~str~~ | +| `lower` | Lowercase form of the word. ~~int~~ | +| `lower_` | Lowercase form of the word. ~~str~~ | +| `shape` | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~int~~ | +| `shape_` | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~str~~ | +| `prefix` | Length-N substring from the start of the word. Defaults to `N=1`. ~~int~~ | +| `prefix_` | Length-N substring from the start of the word. Defaults to `N=1`. ~~str~~ | +| `suffix` | Length-N substring from the end of the word. Defaults to `N=3`. ~~int~~ | +| `suffix_` | Length-N substring from the start of the word. Defaults to `N=3`. ~~str~~ | +| `is_alpha` | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. ~~bool~~ | +| `is_ascii` | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. ~~bool~~ | +| `is_digit` | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. ~~bool~~ | +| `is_lower` | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. ~~bool~~ | +| `is_upper` | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. ~~bool~~ | +| `is_title` | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. ~~bool~~ | +| `is_punct` | Is the lexeme punctuation? ~~bool~~ | +| `is_left_punct` | Is the lexeme a left punctuation mark, e.g. `(`? ~~bool~~ | +| `is_right_punct` | Is the lexeme a right punctuation mark, e.g. `)`? ~~bool~~ | +| `is_space` | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. ~~bool~~ | +| `is_bracket` | Is the lexeme a bracket? ~~bool~~ | +| `is_quote` | Is the lexeme a quotation mark? ~~bool~~ | +| `is_currency` | Is the lexeme a currency symbol? ~~bool~~ | +| `like_url` | Does the lexeme resemble a URL? ~~bool~~ | +| `like_num` | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ | +| `like_email` | Does the lexeme resemble an email address? ~~bool~~ | +| `is_oov` | Is the lexeme out-of-vocabulary (i.e. does it not have a word vector)? ~~bool~~ | +| `is_stop` | Is the lexeme part of a "stop list"? ~~bool~~ | +| `lang` | Language of the parent vocabulary. ~~int~~ | +| `lang_` | Language of the parent vocabulary. ~~str~~ | +| `prob` | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). ~~float~~ | +| `cluster` | Brown cluster ID. ~~int~~ | diff --git a/website/docs/api/matcher.md b/website/docs/api/matcher.md index ff6923cf2..e3fc86e48 100644 --- a/website/docs/api/matcher.md +++ b/website/docs/api/matcher.md @@ -33,7 +33,7 @@ rule-based matching are: | Attribute | Description | | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | | `ORTH` | The exact verbatim text of a token. ~~str~~ | -| `TEXT` 2.1 | The exact verbatim text of a token. ~~str~~ | +| `TEXT` | The exact verbatim text of a token. ~~str~~ | | `NORM` | The normalized form of the token text. ~~str~~ | | `LOWER` | The lowercase form of the token text. ~~str~~ | | `LENGTH` | The length of the token text. ~~int~~ | @@ -48,7 +48,7 @@ rule-based matching are: | `ENT_IOB` | The IOB part of the token's entity tag. ~~str~~ | | `ENT_ID` | The token's entity ID (`ent_id`). ~~str~~ | | `ENT_KB_ID` | The token's entity knowledge base ID (`ent_kb_id`). ~~str~~ | -| `_` 2.1 | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~ | +| `_` | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~ | | `OP` | Operator or quantifier to determine how often to match a token pattern. ~~str~~ | Operators and quantifiers define **how often** a token pattern should be @@ -109,10 +109,10 @@ string where an integer is expected) or unexpected property names. > matcher = Matcher(nlp.vocab) > ``` -| Name | Description | -| --------------------------------------- | ----------------------------------------------------------------------------------------------------- | -| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ | -| `validate` 2.1 | Validate all patterns added to this matcher. ~~bool~~ | +| Name | Description | +| ---------- | ----------------------------------------------------------------------------------------------------- | +| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ | +| `validate` | Validate all patterns added to this matcher. ~~bool~~ | ## Matcher.\_\_call\_\_ {#call tag="method"} diff --git a/website/docs/api/phrasematcher.md b/website/docs/api/phrasematcher.md index b06198916..0ef4e54da 100644 --- a/website/docs/api/phrasematcher.md +++ b/website/docs/api/phrasematcher.md @@ -36,11 +36,11 @@ be shown. > matcher = PhraseMatcher(nlp.vocab) > ``` -| Name | Description | -| --------------------------------------- | ------------------------------------------------------------------------------------------------------ | -| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ | -| `attr` 2.1 | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ | -| `validate` 2.1 | Validate patterns added to the matcher. ~~bool~~ | +| Name | Description | +| ---------- | ------------------------------------------------------------------------------------------------------ | +| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ | +| `attr` | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ | +| `validate` | Validate patterns added to the matcher. ~~bool~~ | ## PhraseMatcher.\_\_call\_\_ {#call tag="method"} diff --git a/website/docs/api/span.md b/website/docs/api/span.md index 9bca0c410..264418006 100644 --- a/website/docs/api/span.md +++ b/website/docs/api/span.md @@ -186,14 +186,14 @@ the character indices don't map to a valid span. > assert span.text == "New York" > ``` -| Name | Description | -| ------------------------------------ | ----------------------------------------------------------------------------------------- | -| `start` | The index of the first character of the span. ~~int~~ | -| `end` | The index of the last character after the span. ~~int~~ | -| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ | -| `kb_id` 2.2 | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ | -| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ | -| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ | +| Name | Description | +| ----------- | ----------------------------------------------------------------------------------------- | +| `start` | The index of the first character of the span. ~~int~~ | +| `end` | The index of the last character after the span. ~~int~~ | +| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ | +| `kb_id` | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ | +| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ | +| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ | ## Span.similarity {#similarity tag="method" model="vectors"} @@ -544,25 +544,25 @@ overlaps with will be returned. ## Attributes {#attributes} -| Name | Description | -| --------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | -| `doc` | The parent document. ~~Doc~~ | -| `tensor` 2.1.7 | The span's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~ | -| `start` | The token offset for the start of the span. ~~int~~ | -| `end` | The token offset for the end of the span. ~~int~~ | -| `start_char` | The character offset for the start of the span. ~~int~~ | -| `end_char` | The character offset for the end of the span. ~~int~~ | -| `text` | A string representation of the span text. ~~str~~ | -| `text_with_ws` | The text content of the span with a trailing whitespace character if the last token has one. ~~str~~ | -| `orth` | ID of the verbatim text content. ~~int~~ | -| `orth_` | Verbatim text content (identical to `Span.text`). Exists mostly for consistency with the other attributes. ~~str~~ | -| `label` | The hash value of the span's label. ~~int~~ | -| `label_` | The span's label. ~~str~~ | -| `lemma_` | The span's lemma. Equivalent to `"".join(token.text_with_ws for token in span)`. ~~str~~ | -| `kb_id` | The hash value of the knowledge base ID referred to by the span. ~~int~~ | -| `kb_id_` | The knowledge base ID referred to by the span. ~~str~~ | -| `ent_id` | Alias for `id`: the hash value of the span's ID. ~~int~~ | -| `ent_id_` | Alias for `id_`: the span's ID. ~~str~~ | -| `id` | The hash value of the span's ID. ~~int~~ | -| `id_` | The span's ID. ~~str~~ | -| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ | +| Name | Description | +| -------------- | ----------------------------------------------------------------------------------------------------------------------------- | +| `doc` | The parent document. ~~Doc~~ | +| `tensor` | The span's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~ | +| `start` | The token offset for the start of the span. ~~int~~ | +| `end` | The token offset for the end of the span. ~~int~~ | +| `start_char` | The character offset for the start of the span. ~~int~~ | +| `end_char` | The character offset for the end of the span. ~~int~~ | +| `text` | A string representation of the span text. ~~str~~ | +| `text_with_ws` | The text content of the span with a trailing whitespace character if the last token has one. ~~str~~ | +| `orth` | ID of the verbatim text content. ~~int~~ | +| `orth_` | Verbatim text content (identical to `Span.text`). Exists mostly for consistency with the other attributes. ~~str~~ | +| `label` | The hash value of the span's label. ~~int~~ | +| `label_` | The span's label. ~~str~~ | +| `lemma_` | The span's lemma. Equivalent to `"".join(token.text_with_ws for token in span)`. ~~str~~ | +| `kb_id` | The hash value of the knowledge base ID referred to by the span. ~~int~~ | +| `kb_id_` | The knowledge base ID referred to by the span. ~~str~~ | +| `ent_id` | Alias for `id`: the hash value of the span's ID. ~~int~~ | +| `ent_id_` | Alias for `id_`: the span's ID. ~~str~~ | +| `id` | The hash value of the span's ID. ~~int~~ | +| `id_` | The span's ID. ~~str~~ | +| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ | diff --git a/website/docs/api/token.md b/website/docs/api/token.md index 6c35d47b1..25155d961 100644 --- a/website/docs/api/token.md +++ b/website/docs/api/token.md @@ -403,74 +403,74 @@ The L2 norm of the token's vector representation. ## Attributes {#attributes} -| Name | Description | -| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `doc` | The parent document. ~~Doc~~ | -| `lex` 3 | The underlying lexeme. ~~Lexeme~~ | -| `sent` 2.0.12 | The sentence span that this token is a part of. ~~Span~~ | -| `text` | Verbatim text content. ~~str~~ | -| `text_with_ws` | Text content, with trailing space character if present. ~~str~~ | -| `whitespace_` | Trailing space character if present. ~~str~~ | -| `orth` | ID of the verbatim text content. ~~int~~ | -| `orth_` | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. ~~str~~ | -| `vocab` | The vocab object of the parent `Doc`. ~~vocab~~ | -| `tensor` 2.1.7 | The token's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~ | -| `head` | The syntactic parent, or "governor", of this token. ~~Token~~ | -| `left_edge` | The leftmost token of this token's syntactic descendants. ~~Token~~ | -| `right_edge` | The rightmost token of this token's syntactic descendants. ~~Token~~ | -| `i` | The index of the token within the parent document. ~~int~~ | -| `ent_type` | Named entity type. ~~int~~ | -| `ent_type_` | Named entity type. ~~str~~ | -| `ent_iob` | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. ~~int~~ | -| `ent_iob_` | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. ~~str~~ | -| `ent_kb_id` 2.2 | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~int~~ | -| `ent_kb_id_` 2.2 | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~str~~ | -| `ent_id` | ID of the entity the token is an instance of, if any. ~~int~~ | -| `ent_id_` | ID of the entity the token is an instance of, if any. ~~str~~ | -| `lemma` | Base form of the token, with no inflectional suffixes. ~~int~~ | -| `lemma_` | Base form of the token, with no inflectional suffixes. ~~str~~ | -| `norm` | The token's norm, i.e. a normalized form of the token text. Can be set in the language's [tokenizer exceptions](/usage/linguistic-features#language-data). ~~int~~ | -| `norm_` | The token's norm, i.e. a normalized form of the token text. Can be set in the language's [tokenizer exceptions](/usage/linguistic-features#language-data). ~~str~~ | -| `lower` | Lowercase form of the token. ~~int~~ | -| `lower_` | Lowercase form of the token text. Equivalent to `Token.text.lower()`. ~~str~~ | -| `shape` | Transform of the token's string to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~int~~ | -| `shape_` | Transform of the token's string to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~str~~ | -| `prefix` | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. ~~int~~ | -| `prefix_` | A length-N substring from the start of the token. Defaults to `N=1`. ~~str~~ | -| `suffix` | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. ~~int~~ | -| `suffix_` | Length-N substring from the end of the token. Defaults to `N=3`. ~~str~~ | -| `is_alpha` | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. ~~bool~~ | -| `is_ascii` | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. ~~bool~~ | -| `is_digit` | Does the token consist of digits? Equivalent to `token.text.isdigit()`. ~~bool~~ | -| `is_lower` | Is the token in lowercase? Equivalent to `token.text.islower()`. ~~bool~~ | -| `is_upper` | Is the token in uppercase? Equivalent to `token.text.isupper()`. ~~bool~~ | -| `is_title` | Is the token in titlecase? Equivalent to `token.text.istitle()`. ~~bool~~ | -| `is_punct` | Is the token punctuation? ~~bool~~ | -| `is_left_punct` | Is the token a left punctuation mark, e.g. `"("` ? ~~bool~~ | -| `is_right_punct` | Is the token a right punctuation mark, e.g. `")"` ? ~~bool~~ | -| `is_sent_start` | Does the token start a sentence? ~~bool~~ or `None` if unknown. Defaults to `True` for the first token in the `Doc`. | -| `is_sent_end` | Does the token end a sentence? ~~bool~~ or `None` if unknown. | -| `is_space` | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. ~~bool~~ | -| `is_bracket` | Is the token a bracket? ~~bool~~ | -| `is_quote` | Is the token a quotation mark? ~~bool~~ | -| `is_currency` 2.0.8 | Is the token a currency symbol? ~~bool~~ | -| `like_url` | Does the token resemble a URL? ~~bool~~ | -| `like_num` | Does the token represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ | -| `like_email` | Does the token resemble an email address? ~~bool~~ | -| `is_oov` | Is the token out-of-vocabulary (i.e. does it not have a word vector)? ~~bool~~ | -| `is_stop` | Is the token part of a "stop list"? ~~bool~~ | -| `pos` | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/u/pos/). ~~int~~ | -| `pos_` | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/u/pos/). ~~str~~ | -| `tag` | Fine-grained part-of-speech. ~~int~~ | -| `tag_` | Fine-grained part-of-speech. ~~str~~ | -| `morph` 3 | Morphological analysis. ~~MorphAnalysis~~ | -| `dep` | Syntactic dependency relation. ~~int~~ | -| `dep_` | Syntactic dependency relation. ~~str~~ | -| `lang` | Language of the parent document's vocabulary. ~~int~~ | -| `lang_` | Language of the parent document's vocabulary. ~~str~~ | -| `prob` | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). ~~float~~ | -| `idx` | The character offset of the token within the parent document. ~~int~~ | -| `lex_id` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | -| `rank` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | -| `cluster` | Brown cluster ID. ~~int~~ | -| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ | +| Name | Description | +| ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `doc` | The parent document. ~~Doc~~ | +| `lex` 3 | The underlying lexeme. ~~Lexeme~~ | +| `sent` | The sentence span that this token is a part of. ~~Span~~ | +| `text` | Verbatim text content. ~~str~~ | +| `text_with_ws` | Text content, with trailing space character if present. ~~str~~ | +| `whitespace_` | Trailing space character if present. ~~str~~ | +| `orth` | ID of the verbatim text content. ~~int~~ | +| `orth_` | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. ~~str~~ | +| `vocab` | The vocab object of the parent `Doc`. ~~vocab~~ | +| `tensor` | The token's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~ | +| `head` | The syntactic parent, or "governor", of this token. ~~Token~~ | +| `left_edge` | The leftmost token of this token's syntactic descendants. ~~Token~~ | +| `right_edge` | The rightmost token of this token's syntactic descendants. ~~Token~~ | +| `i` | The index of the token within the parent document. ~~int~~ | +| `ent_type` | Named entity type. ~~int~~ | +| `ent_type_` | Named entity type. ~~str~~ | +| `ent_iob` | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. ~~int~~ | +| `ent_iob_` | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. ~~str~~ | +| `ent_kb_id` | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~int~~ | +| `ent_kb_id_` | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~str~~ | +| `ent_id` | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. ~~int~~ | +| `ent_id_` | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. ~~str~~ | +| `lemma` | Base form of the token, with no inflectional suffixes. ~~int~~ | +| `lemma_` | Base form of the token, with no inflectional suffixes. ~~str~~ | +| `norm` | The token's norm, i.e. a normalized form of the token text. Can be set in the language's [tokenizer exceptions](/usage/linguistic-features#language-data). ~~int~~ | +| `norm_` | The token's norm, i.e. a normalized form of the token text. Can be set in the language's [tokenizer exceptions](/usage/linguistic-features#language-data). ~~str~~ | +| `lower` | Lowercase form of the token. ~~int~~ | +| `lower_` | Lowercase form of the token text. Equivalent to `Token.text.lower()`. ~~str~~ | +| `shape` | Transform of the token's string to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~int~~ | +| `shape_` | Transform of the token's string to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~str~~ | +| `prefix` | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. ~~int~~ | +| `prefix_` | A length-N substring from the start of the token. Defaults to `N=1`. ~~str~~ | +| `suffix` | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. ~~int~~ | +| `suffix_` | Length-N substring from the end of the token. Defaults to `N=3`. ~~str~~ | +| `is_alpha` | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. ~~bool~~ | +| `is_ascii` | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. ~~bool~~ | +| `is_digit` | Does the token consist of digits? Equivalent to `token.text.isdigit()`. ~~bool~~ | +| `is_lower` | Is the token in lowercase? Equivalent to `token.text.islower()`. ~~bool~~ | +| `is_upper` | Is the token in uppercase? Equivalent to `token.text.isupper()`. ~~bool~~ | +| `is_title` | Is the token in titlecase? Equivalent to `token.text.istitle()`. ~~bool~~ | +| `is_punct` | Is the token punctuation? ~~bool~~ | +| `is_left_punct` | Is the token a left punctuation mark, e.g. `"("` ? ~~bool~~ | +| `is_right_punct` | Is the token a right punctuation mark, e.g. `")"` ? ~~bool~~ | +| `is_sent_start` | Does the token start a sentence? ~~bool~~ or `None` if unknown. Defaults to `True` for the first token in the `Doc`. | +| `is_sent_end` | Does the token end a sentence? ~~bool~~ or `None` if unknown. | +| `is_space` | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. ~~bool~~ | +| `is_bracket` | Is the token a bracket? ~~bool~~ | +| `is_quote` | Is the token a quotation mark? ~~bool~~ | +| `is_currency` | Is the token a currency symbol? ~~bool~~ | +| `like_url` | Does the token resemble a URL? ~~bool~~ | +| `like_num` | Does the token represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ | +| `like_email` | Does the token resemble an email address? ~~bool~~ | +| `is_oov` | Is the token out-of-vocabulary (i.e. does it not have a word vector)? ~~bool~~ | +| `is_stop` | Is the token part of a "stop list"? ~~bool~~ | +| `pos` | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/u/pos/). ~~int~~ | +| `pos_` | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/u/pos/). ~~str~~ | +| `tag` | Fine-grained part-of-speech. ~~int~~ | +| `tag_` | Fine-grained part-of-speech. ~~str~~ | +| `morph` 3 | Morphological analysis. ~~MorphAnalysis~~ | +| `dep` | Syntactic dependency relation. ~~int~~ | +| `dep_` | Syntactic dependency relation. ~~str~~ | +| `lang` | Language of the parent document's vocabulary. ~~int~~ | +| `lang_` | Language of the parent document's vocabulary. ~~str~~ | +| `prob` | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). ~~float~~ | +| `idx` | The character offset of the token within the parent document. ~~int~~ | +| `lex_id` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | +| `rank` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | +| `cluster` | Brown cluster ID. ~~int~~ | +| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ | diff --git a/website/docs/api/top-level.md b/website/docs/api/top-level.md index bc53fc868..26a5d42f4 100644 --- a/website/docs/api/top-level.md +++ b/website/docs/api/top-level.md @@ -45,16 +45,16 @@ specified separately using the new `exclude` keyword argument. > nlp = spacy.load("en_core_web_sm", exclude=["parser", "tagger"]) > ``` -| Name | Description | -| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| `name` | Pipeline to load, i.e. package name or path. ~~Union[str, Path]~~ | -| _keyword-only_ | | -| `vocab` | Optional shared vocab to pass in on initialization. If `True` (default), a new `Vocab` object will be created. ~~Union[Vocab, bool]~~ | -| `disable` | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ | -| `enable` 3.4 | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled. ~~Union[str, Iterable[str]]~~ | -| `exclude` 3 | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~ | -| `config` 3 | Optional config overrides, either as nested dict or dict keyed by section value in dot notation, e.g. `"components.name.value"`. ~~Union[Dict[str, Any], Config]~~ | -| **RETURNS** | A `Language` object with the loaded pipeline. ~~Language~~ | +| Name | Description | +| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `name` | Pipeline to load, i.e. package name or path. ~~Union[str, Path]~~ | +| _keyword-only_ | | +| `vocab` | Optional shared vocab to pass in on initialization. If `True` (default), a new `Vocab` object will be created. ~~Union[Vocab, bool]~~ | +| `disable` | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). Is merged with the config entry `nlp.disabled`. ~~Union[str, Iterable[str]]~~ | +| `enable` 3.4 | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled. ~~Union[str, Iterable[str]]~~ | +| `exclude` 3 | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~ | +| `config` 3 | Optional config overrides, either as nested dict or dict keyed by section value in dot notation, e.g. `"components.name.value"`. ~~Union[Dict[str, Any], Config]~~ | +| **RETURNS** | A `Language` object with the loaded pipeline. ~~Language~~ | Essentially, `spacy.load()` is a convenience wrapper that reads the pipeline's [`config.cfg`](/api/data-formats#config), uses the language and pipeline @@ -354,22 +354,22 @@ If a setting is not present in the options, the default value will be used. > displacy.serve(doc, style="dep", options=options) > ``` -| Name | Description | -| ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- | -| `fine_grained` | Use fine-grained part-of-speech tags (`Token.tag_`) instead of coarse-grained tags (`Token.pos_`). Defaults to `False`. ~~bool~~ | -| `add_lemma` 2.2.4 | Print the lemmas in a separate row below the token texts. Defaults to `False`. ~~bool~~ | -| `collapse_punct` | Attach punctuation to tokens. Can make the parse more readable, as it prevents long arcs to attach punctuation. Defaults to `True`. ~~bool~~ | -| `collapse_phrases` | Merge noun phrases into one token. Defaults to `False`. ~~bool~~ | -| `compact` | "Compact mode" with square arrows that takes up less space. Defaults to `False`. ~~bool~~ | -| `color` | Text color (HEX, RGB or color names). Defaults to `"#000000"`. ~~str~~ | -| `bg` | Background color (HEX, RGB or color names). Defaults to `"#ffffff"`. ~~str~~ | -| `font` | Font name or font family for all text. Defaults to `"Arial"`. ~~str~~ | -| `offset_x` | Spacing on left side of the SVG in px. Defaults to `50`. ~~int~~ | -| `arrow_stroke` | Width of arrow path in px. Defaults to `2`. ~~int~~ | -| `arrow_width` | Width of arrow head in px. Defaults to `10` in regular mode and `8` in compact mode. ~~int~~ | -| `arrow_spacing` | Spacing between arrows in px to avoid overlaps. Defaults to `20` in regular mode and `12` in compact mode. ~~int~~ | -| `word_spacing` | Vertical spacing between words and arcs in px. Defaults to `45`. ~~int~~ | -| `distance` | Distance between words in px. Defaults to `175` in regular mode and `150` in compact mode. ~~int~~ | +| Name | Description | +| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- | +| `fine_grained` | Use fine-grained part-of-speech tags (`Token.tag_`) instead of coarse-grained tags (`Token.pos_`). Defaults to `False`. ~~bool~~ | +| `add_lemma` | Print the lemmas in a separate row below the token texts. Defaults to `False`. ~~bool~~ | +| `collapse_punct` | Attach punctuation to tokens. Can make the parse more readable, as it prevents long arcs to attach punctuation. Defaults to `True`. ~~bool~~ | +| `collapse_phrases` | Merge noun phrases into one token. Defaults to `False`. ~~bool~~ | +| `compact` | "Compact mode" with square arrows that takes up less space. Defaults to `False`. ~~bool~~ | +| `color` | Text color (HEX, RGB or color names). Defaults to `"#000000"`. ~~str~~ | +| `bg` | Background color (HEX, RGB or color names). Defaults to `"#ffffff"`. ~~str~~ | +| `font` | Font name or font family for all text. Defaults to `"Arial"`. ~~str~~ | +| `offset_x` | Spacing on left side of the SVG in px. Defaults to `50`. ~~int~~ | +| `arrow_stroke` | Width of arrow path in px. Defaults to `2`. ~~int~~ | +| `arrow_width` | Width of arrow head in px. Defaults to `10` in regular mode and `8` in compact mode. ~~int~~ | +| `arrow_spacing` | Spacing between arrows in px to avoid overlaps. Defaults to `20` in regular mode and `12` in compact mode. ~~int~~ | +| `word_spacing` | Vertical spacing between words and arcs in px. Defaults to `45`. ~~int~~ | +| `distance` | Distance between words in px. Defaults to `175` in regular mode and `150` in compact mode. ~~int~~ | #### Named Entity Visualizer options {#displacy_options-ent} @@ -385,7 +385,7 @@ If a setting is not present in the options, the default value will be used. | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `ents` | Entity types to highlight or `None` for all types (default). ~~Optional[List[str]]~~ | | `colors` | Color overrides. Entity types should be mapped to color names or values. ~~Dict[str, str]~~ | -| `template` 2.2 | Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use `{bg}`, `{text}` and `{label}`. See [`templates.py`](%%GITHUB_SPACY/spacy/displacy/templates.py) for examples. ~~Optional[str]~~ | +| `template` | Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use `{bg}`, `{text}` and `{label}`. See [`templates.py`](%%GITHUB_SPACY/spacy/displacy/templates.py) for examples. ~~Optional[str]~~ | | `kb_url_template` 3.2.1 | Optional template to construct the KB url for the entity to link to. Expects a python f-string format with single field to fill in. ~~Optional[str]~~ | #### Span Visualizer options {#displacy_options-span} @@ -1004,6 +1004,54 @@ This method was previously available as `spacy.gold.spans_from_biluo_tags`. | `tags` | A sequence of [BILUO](/usage/linguistic-features#accessing-ner) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. ~~List[str]~~ | | **RETURNS** | A sequence of `Span` objects with added entity labels. ~~List[Span]~~ | +### training.biluo_to_iob {#biluo_to_iob tag="function"} + +Convert a sequence of [BILUO](/usage/linguistic-features#accessing-ner) tags to +[IOB](/usage/linguistic-features#accessing-ner) tags. This is useful if you want +use the BILUO tags with a model that only supports IOB tags. + +> #### Example +> +> ```python +> from spacy.training import biluo_to_iob +> +> tags = ["O", "O", "B-LOC", "I-LOC", "L-LOC", "O"] +> iob_tags = biluo_to_iob(tags) +> assert iob_tags == ["O", "O", "B-LOC", "I-LOC", "I-LOC", "O"] +> ``` + +| Name | Description | +| ----------- | --------------------------------------------------------------------------------------- | +| `tags` | A sequence of [BILUO](/usage/linguistic-features#accessing-ner) tags. ~~Iterable[str]~~ | +| **RETURNS** | A list of [IOB](/usage/linguistic-features#accessing-ner) tags. ~~List[str]~~ | + +### training.iob_to_biluo {#iob_to_biluo tag="function"} + +Convert a sequence of [IOB](/usage/linguistic-features#accessing-ner) tags to +[BILUO](/usage/linguistic-features#accessing-ner) tags. This is useful if you +want use the IOB tags with a model that only supports BILUO tags. + + + +This method was previously available as `spacy.gold.iob_to_biluo`. + + + +> #### Example +> +> ```python +> from spacy.training import iob_to_biluo +> +> tags = ["O", "O", "B-LOC", "I-LOC", "O"] +> biluo_tags = iob_to_biluo(tags) +> assert biluo_tags == ["O", "O", "B-LOC", "L-LOC", "O"] +> ``` + +| Name | Description | +| ----------- | ------------------------------------------------------------------------------------- | +| `tags` | A sequence of [IOB](/usage/linguistic-features#accessing-ner) tags. ~~Iterable[str]~~ | +| **RETURNS** | A list of [BILUO](/usage/linguistic-features#accessing-ner) tags. ~~List[str]~~ | + ## Utility functions {#util source="spacy/util.py"} spaCy comes with a small collection of utility functions located in diff --git a/website/docs/api/vectors.md b/website/docs/api/vectors.md index 9636ea04c..d4702b592 100644 --- a/website/docs/api/vectors.md +++ b/website/docs/api/vectors.md @@ -50,7 +50,7 @@ modified later. | _keyword-only_ | | | `strings` | The string store. A new string store is created if one is not provided. Defaults to `None`. ~~Optional[StringStore]~~ | | `shape` | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. ~~Tuple[int, int]~~ | -| `data` | The vector data. ~~numpy.ndarray[ndim=1, dtype=float32]~~ | +| `data` | The vector data. ~~numpy.ndarray[ndim=2, dtype=float32]~~ | | `keys` | A sequence of keys aligned with the data. ~~Iterable[Union[str, int]]~~ | | `name` | A name to identify the vectors table. ~~str~~ | | `mode` 3.2 | Vectors mode: `"default"` or [`"floret"`](https://github.com/explosion/floret) (default: `"default"`). ~~str~~ | diff --git a/website/docs/api/vocab.md b/website/docs/api/vocab.md index 2e4a206ec..5e4de219a 100644 --- a/website/docs/api/vocab.md +++ b/website/docs/api/vocab.md @@ -21,15 +21,15 @@ Create the vocabulary. > vocab = Vocab(strings=["hello", "world"]) > ``` -| Name | Description | -| ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `lex_attr_getters` | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. ~~Optional[Dict[str, Callable[[str], Any]]]~~ | -| `strings` | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. ~~Union[List[str], StringStore]~~ | -| `lookups` | A [`Lookups`](/api/lookups) that stores the `lexeme_norm` and other large lookup tables. Defaults to `None`. ~~Optional[Lookups]~~ | -| `oov_prob` | The default OOV probability. Defaults to `-20.0`. ~~float~~ | -| `vectors_name` 2.2 | A name to identify the vectors table. ~~str~~ | -| `writing_system` | A dictionary describing the language's writing system. Typically provided by [`Language.Defaults`](/api/language#defaults). ~~Dict[str, Any]~~ | -| `get_noun_chunks` | A function that yields base noun phrases used for [`Doc.noun_chunks`](/api/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ | +| Name | Description | +| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `lex_attr_getters` | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. ~~Optional[Dict[str, Callable[[str], Any]]]~~ | +| `strings` | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. ~~Union[List[str], StringStore]~~ | +| `lookups` | A [`Lookups`](/api/lookups) that stores the `lexeme_norm` and other large lookup tables. Defaults to `None`. ~~Optional[Lookups]~~ | +| `oov_prob` | The default OOV probability. Defaults to `-20.0`. ~~float~~ | +| `vectors_name` | A name to identify the vectors table. ~~str~~ | +| `writing_system` | A dictionary describing the language's writing system. Typically provided by [`Language.Defaults`](/api/language#defaults). ~~Dict[str, Any]~~ | +| `get_noun_chunks` | A function that yields base noun phrases used for [`Doc.noun_chunks`](/api/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ | ## Vocab.\_\_len\_\_ {#len tag="method"} @@ -308,14 +308,14 @@ Load state from a binary string. > assert type(PERSON) == int > ``` -| Name | Description | -| ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `strings` | A table managing the string-to-int mapping. ~~StringStore~~ | -| `vectors` 2 | A table associating word IDs to word vectors. ~~Vectors~~ | -| `vectors_length` | Number of dimensions for each word vector. ~~int~~ | -| `lookups` | The available lookup tables in this vocab. ~~Lookups~~ | -| `writing_system` 2.1 | A dict with information about the language's writing system. ~~Dict[str, Any]~~ | -| `get_noun_chunks` 3.0 | A function that yields base noun phrases used for [`Doc.noun_chunks`](/ap/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ | +| Name | Description | +| ---------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `strings` | A table managing the string-to-int mapping. ~~StringStore~~ | +| `vectors` | A table associating word IDs to word vectors. ~~Vectors~~ | +| `vectors_length` | Number of dimensions for each word vector. ~~int~~ | +| `lookups` | The available lookup tables in this vocab. ~~Lookups~~ | +| `writing_system` | A dict with information about the language's writing system. ~~Dict[str, Any]~~ | +| `get_noun_chunks` 3.0 | A function that yields base noun phrases used for [`Doc.noun_chunks`](/api/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ | ## Serialization fields {#serialization-fields} diff --git a/website/docs/styleguide.md b/website/docs/styleguide.md index ed6f9d99b..47bca1ed4 100644 --- a/website/docs/styleguide.md +++ b/website/docs/styleguide.md @@ -8,9 +8,7 @@ menu: - ['Typography', 'typography'] - ['Elements', 'elements'] - ['Components', 'components'] - - ['Setup & Installation', 'setup'] - ['Markdown Reference', 'markdown'] - - ['Project Structure', 'structure'] - ['Editorial', 'editorial'] sidebar: - label: Styleguide @@ -25,6 +23,610 @@ sidebar: url: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md --- -import Readme from 'README.md' +The [spacy.io](https://spacy.io) website is implemented using +[Gatsby](https://www.gatsbyjs.org) with +[Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This +allows authoring content in **straightforward Markdown** without the usual +limitations. Standard elements can be overwritten with powerful +[React](http://reactjs.org/) components and wherever Markdown syntax isn't +enough, JSX components can be used. - +> #### Contributing to the site +> +> The docs can always use another example or more detail, and they should always +> be up to date and not misleading. We always appreciate a +> [pull request](https://github.com/explosion/spaCy/pulls). To quickly find the +> correct file to edit, simply click on the "Suggest edits" button at the bottom +> of a page. +> +> For more details on editing the site locally, see the installation +> instructions and markdown reference below. + +## Logo {#logo source="website/src/images/logo.svg"} + +import { Logos } from 'widgets/styleguide' + +If you would like to use the spaCy logo on your site, please get in touch and +ask us first. However, if you want to show support and tell others that your +project is using spaCy, you can grab one of our +[spaCy badges](/usage/spacy-101#faq-project-with-spacy). + + + +## Colors {#colors} + +import { Colors, Patterns } from 'widgets/styleguide' + + + +### Patterns + + + +## Typography {#typography} + +import { H1, H2, H3, H4, H5, Label, InlineList, Comment } from +'components/typography' + +> #### Markdown +> +> ```markdown_ +> ## Headline 2 +> ## Headline 2 {#some_id} +> ## Headline 2 {#some_id tag="method"} +> ``` +> +> #### JSX +> +> ```jsx +>

Headline 2

+>

Headline 2

+>

Headline 2

+> ``` + +Headlines are set in +[HK Grotesk](http://cargocollective.com/hanken/HK-Grotesk-Open-Source-Font) by +Hanken Design. All other body text and code uses the best-matching default +system font to provide a "native" reading experience. All code uses the +[JetBrains Mono](https://www.jetbrains.com/lp/mono/) typeface by JetBrains. + + + +Level 2 headings are automatically wrapped in `
` elements at compile +time, using a custom +[Markdown transformer](https://github.com/explosion/spaCy/tree/master/website/plugins/remark-wrap-section.js). +This makes it easier to highlight the section that's currently in the viewpoint +in the sidebar menu. + + + +
+

Headline 1

+

Headline 2

+

Headline 3

+

Headline 4

+
Headline 5
+ +
+ +--- + +The following optional attributes can be set on the headline to modify it. For +example, to add a tag for the documented type or mark features that have been +introduced in a specific version or require statistical models to be loaded. +Tags are also available as standalone `` components. + +| Argument | Example | Result | +| -------- | -------------------------- | ----------------------------------------- | +| `tag` | `{tag="method"}` | method | +| `new` | `{new="3"}` | 3 | +| `model` | `{model="tagger, parser"}` | tagger, parser | +| `hidden` | `{hidden="true"}` | | + +## Elements {#elements} + +### Links {#links} + +> #### Markdown +> +> ```markdown +> [I am a link](https://spacy.io) +> ``` +> +> #### JSX +> +> ```jsx +> I am a link +> ``` + +Special link styles are used depending on the link URL. + +- [I am a regular external link](https://explosion.ai) +- [I am a link to the documentation](/api/doc) +- [I am a link to an architecture](/api/architectures#HashEmbedCNN) +- [I am a link to a model](/models/en#en_core_web_sm) +- [I am a link to GitHub](https://github.com/explosion/spaCy) + +### Abbreviations {#abbr} + +import { Abbr } from 'components/typography' + +> #### JSX +> +> ```jsx +> Abbreviation +> ``` + +Some text with an abbreviation. On small +screens, I collapse and the explanation text is displayed next to the +abbreviation. + +### Tags {#tags} + +import Tag from 'components/tag' + +> ```jsx +> method +> 4 +> tagger, parser +> ``` + +Tags can be used together with headlines, or next to properties across the +documentation, and combined with tooltips to provide additional information. An +optional `variant` argument can be used for special tags. `variant="new"` makes +the tag take a version number to mark new features. Using the component, +visibility of this tag can later be toggled once the feature isn't considered +new anymore. Setting `variant="model"` takes a description of model capabilities +and can be used to mark features that require a respective model to be +installed. + + + +method 4 tagger, +parser + + + +### Buttons {#buttons} + +import Button from 'components/button' + +> ```jsx +> +> +> ``` + +Link buttons come in two variants, `primary` and `secondary` and two sizes, with +an optional `large` size modifier. Since they're mostly used as enhanced links, +the buttons are implemented as styled links instead of native button elements. + + + + +
+ + + + +## Components + +### Table {#table} + +> #### Markdown +> +> ```markdown_ +> | Header 1 | Header 2 | +> | -------- | -------- | +> | Column 1 | Column 2 | +> ``` +> +> #### JSX +> +> ```markup +> +> +> +>
Header 1Header 2
Column 1Column 2
+> ``` + +Tables are used to present data and API documentation. Certain keywords can be +used to mark a footer row with a distinct style, for example to visualize the +return values of a documented function. + +| Header 1 | Header 2 | Header 3 | Header 4 | +| ----------- | -------- | :------: | -------: | +| Column 1 | Column 2 | Column 3 | Column 4 | +| Column 1 | Column 2 | Column 3 | Column 4 | +| Column 1 | Column 2 | Column 3 | Column 4 | +| Column 1 | Column 2 | Column 3 | Column 4 | +| **RETURNS** | Column 2 | Column 3 | Column 4 | + +Tables also support optional "divider" rows that are typically used to denote +keyword-only arguments in API documentation. To turn a row into a dividing +headline, it should only include content in its first cell, and its value should +be italicized: + +> #### Markdown +> +> ```markdown_ +> | Header 1 | Header 2 | Header 3 | +> | -------- | -------- | -------- | +> | Column 1 | Column 2 | Column 3 | +> | _Hello_ | | | +> | Column 1 | Column 2 | Column 3 | +> ``` + +| Header 1 | Header 2 | Header 3 | +| -------- | -------- | -------- | +| Column 1 | Column 2 | Column 3 | +| _Hello_ | | | +| Column 1 | Column 2 | Column 3 | + +### Type Annotations {#type-annotations} + +> #### Markdown +> +> ```markdown_ +> ~~Model[List[Doc], Floats2d]~~ +> ``` +> +> #### JSX +> +> ```markup +> Model[List[Doc], Floats2d] +> ``` + +Type annotations are special inline code blocks are used to describe Python +types in the [type hints](https://docs.python.org/3/library/typing.html) format. +The special component will split the type, apply syntax highlighting and link +all types that specify links in `meta/type-annotations.json`. Types can link to +internal or external documentation pages. To make it easy to represent the type +annotations in Markdown, the rendering "hijacks" the `~~` tags that would +typically be converted to a `` element – but in this case, text surrounded +by `~~` becomes a type annotation. + +- ~~Dict[str, List[Union[Doc, Span]]]~~ +- ~~Model[List[Doc], List[numpy.ndarray]]~~ + +Type annotations support a special visual style in tables and will render as a +separate row, under the cell text. This allows the API docs to display complex +types without taking up too much space in the cell. The type annotation should +always be the **last element** in the row. + +> #### Markdown +> +> ```markdown_ +> | Header 1 | Header 2 | +> | -------- | ----------------------- | +> | Column 1 | Column 2 ~~List[Doc]~~ | +> ``` + +| Name | Description | +| ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `vocab` | The shared vocabulary. ~~Vocab~~ | +| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. ~~Model[List[Doc], FullTransformerBatch]~~ | +| `set_extra_annotations` | Function that takes a batch of `Doc` objects and transformer outputs and can set additional annotations on the `Doc`. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ | + +### List {#list} + +> #### Markdown +> +> ```markdown_ +> 1. One +> 2. Two +> ``` +> +> #### JSX +> +> ```markup +>
    +>
  1. One
    2. +>
  2. Two
    3. +>
+> ``` + +Lists are available as bulleted and numbered. Markdown lists are transformed +automatically. + +- I am a bulleted list +- I have nice bullets +- Lorem ipsum dolor +- consectetur adipiscing elit + +1. I am an ordered list +2. I have nice numbers +3. Lorem ipsum dolor +4. consectetur adipiscing elit + +### Aside {#aside} + +> #### Markdown +> +> ```markdown_ +> > #### Aside title +> > This is aside text. +> ``` +> +> #### JSX +> +> ```jsx +> +> ``` + +Asides can be used to display additional notes and content in the right-hand +column. Asides can contain text, code and other elements if needed. Visually, +asides are moved to the side on the X-axis, and displayed at the same level they +were inserted. On small screens, they collapse and are rendered in their +original position, in between the text. + +To make them easier to use in Markdown, paragraphs formatted as blockquotes will +turn into asides by default. Level 4 headlines (with a leading `####`) will +become aside titles. + +### Code Block {#code-block} + +> #### Markdown +> +> ````markdown_ +> ```python +> ### This is a title +> import spacy +> ``` +> ```` +> +> #### JSX +> +> ```jsx +> +> import spacy +> +> ``` + +Code blocks use the [Prism](http://prismjs.com/) syntax highlighter with a +custom theme. The language can be set individually on each block, and defaults +to raw text with no highlighting. An optional label can be added as the first +line with the prefix `####` (Python-like) and `///` (JavaScript-like). the +indented block as plain text and preserve whitespace. + +```python +### Using spaCy +import spacy +nlp = spacy.load("en_core_web_sm") +doc = nlp("This is a sentence.") +for token in doc: + print(token.text, token.pos_) +``` + +Code blocks and also specify an optional range of line numbers to highlight by +adding `{highlight="..."}` to the headline. Acceptable ranges are spans like +`5-7`, but also `5-7,10` or `5-7,10,13-14`. + +> #### Markdown +> +> ````markdown_ +> ```python +> ### This is a title {highlight="1-2"} +> import spacy +> nlp = spacy.load("en_core_web_sm") +> ``` +> ```` + +```python +### Using the matcher {highlight="5-7"} +import spacy +from spacy.matcher import Matcher + +nlp = spacy.load('en_core_web_sm') +matcher = Matcher(nlp.vocab) +pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}] +matcher.add("HelloWorld", None, pattern) +doc = nlp("Hello, world! Hello world!") +matches = matcher(doc) +``` + +Adding `{executable="true"}` to the title turns the code into an executable +block, powered by [Binder](https://mybinder.org) and +[Juniper](https://github.com/ines/juniper). If JavaScript is disabled, the +interactive widget defaults to a regular code block. + +> #### Markdown +> +> ````markdown_ +> ```python +> ### {executable="true"} +> import spacy +> nlp = spacy.load("en_core_web_sm") +> ``` +> ```` + +```python +### {executable="true"} +import spacy +nlp = spacy.load("en_core_web_sm") +doc = nlp("This is a sentence.") +for token in doc: + print(token.text, token.pos_) +``` + +If a code block only contains a URL to a GitHub file, the raw file contents are +embedded automatically and syntax highlighting is applied. The link to the +original file is shown at the top of the widget. + +> #### Markdown +> +> ````markdown_ +> ```python +> https://github.com/... +> ``` +> ```` +> +> #### JSX +> +> ```jsx +> +> ``` + +```python +https://github.com/explosion/spaCy/tree/master/spacy/language.py +``` + +### Infobox {#infobox} + +import Infobox from 'components/infobox' + +> #### JSX +> +> ```jsx +> Regular infobox +> This is a warning. +> This is dangerous. +> ``` + +Infoboxes can be used to add notes, updates, warnings or additional information +to a page or section. Semantically, they're implemented and interpreted as an +`aside` element. Infoboxes can take an optional `title` argument, as well as an +optional `variant` (either `"warning"` or `"danger"`). + + + +If needed, an infobox can contain regular text, `inline code`, lists and other +blocks. + + + + + +If needed, an infobox can contain regular text, `inline code`, lists and other +blocks. + + + + + +If needed, an infobox can contain regular text, `inline code`, lists and other +blocks. + + + +### Accordion {#accordion} + +import Accordion from 'components/accordion' + +> #### JSX +> +> ```jsx +> +> Accordion content goes here. +> +> ``` + +Accordions are collapsible sections that are mostly used for lengthy tables, +like the tag and label annotation schemes for different languages. They all need +to be presented – but chances are the user doesn't actually care about _all_ of +them, especially not at the same time. So it's fairly reasonable to hide them +begin a click. This particular implementation was inspired by the amazing +[Inclusive Components blog](https://inclusive-components.design/collapsible-sections/). + + + +Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante, +pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt +nisi. Integer commodo pellentesque tincidunt. Nam at turpis finibus tortor +gravida sodales tincidunt sit amet est. Nullam euismod arcu in tortor auctor, +sit amet dignissim justo congue. + + + +## Markdown reference {#markdown} + +All page content and page meta lives in the `.md` files in the `/docs` +directory. The frontmatter block at the top of each file defines the page title +and other settings like the sidebar menu. + +````markdown +--- +title: Page title +--- + +## Headline starting a section {#some_id} + +This is a regular paragraph with a [link](https://spacy.io) and **bold text**. + +> #### This is an aside title +> +> This is aside text. + +### Subheadline + +| Header 1 | Header 2 | +| -------- | -------- | +| Column 1 | Column 2 | + +```python +### Code block title {highlight="2-3"} +import spacy +nlp = spacy.load("en_core_web_sm") +doc = nlp("Hello world") +``` + + + +This is content in the infobox. + + +```` + +In addition to the native markdown elements, you can use the components +[``][infobox], [``][accordion], [``][abbr] and +[``][tag] via their JSX syntax. + +[infobox]: https://spacy.io/styleguide#infobox +[accordion]: https://spacy.io/styleguide#accordion +[abbr]: https://spacy.io/styleguide#abbr +[tag]: https://spacy.io/styleguide#tag + +## Editorial {#editorial} + +- "spaCy" should always be spelled with a lowercase "s" and a capital "C", + unless it specifically refers to the Python package or Python import `spacy` + (in which case it should be formatted as code). + - ✅ spaCy is a library for advanced NLP in Python. + - ❌ Spacy is a library for advanced NLP in Python. + - ✅ First, you need to install the `spacy` package from pip. +- Mentions of code, like function names, classes, variable names etc. in inline + text should be formatted as `code`. + - ✅ "Calling the `nlp` object on a text returns a `Doc`." +- Objects that have pages in the [API docs](/api) should be linked – for + example, [`Doc`](/api/doc) or [`Language.to_disk`](/api/language#to_disk). The + mentions should still be formatted as code within the link. Links pointing to + the API docs will automatically receive a little icon. However, if a paragraph + includes many references to the API, the links can easily get messy. In that + case, we typically only link the first mention of an object and not any + subsequent ones. + - ✅ The [`Span`](/api/span) and [`Token`](/api/token) objects are views of a + [`Doc`](/api/doc). [`Span.as_doc`](/api/span#as_doc) creates a `Doc` object + from a `Span`. + - ❌ The [`Span`](/api/span) and [`Token`](/api/token) objects are views of a + [`Doc`](/api/doc). [`Span.as_doc`](/api/span#as_doc) creates a + [`Doc`](/api/doc) object from a [`Span`](/api/span). + +* Other things we format as code are: references to trained pipeline packages + like `en_core_web_sm` or file names like `code.py` or `meta.json`. + + - ✅ After training, the `config.cfg` is saved to disk. + +* [Type annotations](#type-annotations) are a special type of code formatting, + expressed by wrapping the text in `~~` instead of backticks. The result looks + like this: ~~List[Doc]~~. All references to known types will be linked + automatically. + + - ✅ The model has the input type ~~List[Doc]~~ and it outputs a + ~~List[Array2d]~~. + +* We try to keep links meaningful but short. + - ✅ For details, see the usage guide on + [training with custom code](/usage/training#custom-code). + - ❌ For details, see + [the usage guide on training with custom code](/usage/training#custom-code). + - ❌ For details, see the usage guide on training with custom code + [here](/usage/training#custom-code). diff --git a/website/docs/usage/index.md b/website/docs/usage/index.md index 1f4869606..d6ad6681c 100644 --- a/website/docs/usage/index.md +++ b/website/docs/usage/index.md @@ -75,7 +75,6 @@ spaCy's [`setup.cfg`](%%GITHUB_SPACY/setup.cfg) for details on what's included. | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `lookups` | Install [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) for data tables for lemmatization and lexeme normalization. The data is serialized with trained pipelines, so you only need this package if you want to train your own models. | | `transformers` | Install [`spacy-transformers`](https://github.com/explosion/spacy-transformers). The package will be installed automatically when you install a transformer-based pipeline. | -| `ray` | Install [`spacy-ray`](https://github.com/explosion/spacy-ray) to add CLI commands for [parallel training](/usage/training#parallel-training). | | `cuda`, ... | Install spaCy with GPU support provided by [CuPy](https://cupy.chainer.org) for your given CUDA version. See the GPU [installation instructions](#gpu) for details and options. | | `apple` | Install [`thinc-apple-ops`](https://github.com/explosion/thinc-apple-ops) to improve performance on an Apple M1. | | `ja`, `ko`, `th` | Install additional dependencies required for tokenization for the [languages](/usage/models#languages). | @@ -236,10 +235,10 @@ package to see what the oldest recommended versions of `numpy` are. Some additional options may be useful for spaCy developers who are editing the source code and recompiling frequently. -- Install in editable mode. Changes to `.py` files will be reflected as soon - as the files are saved, but edits to Cython files (`.pxd`, `.pyx`) will - require the `pip install` command below to be run again. Before installing in - editable mode, be sure you have removed any previous installs with +- Install in editable mode. Changes to `.py` files will be reflected as soon as + the files are saved, but edits to Cython files (`.pxd`, `.pyx`) will require + the `pip install` command below to be run again. Before installing in editable + mode, be sure you have removed any previous installs with `pip uninstall spacy`, which you may need to run multiple times to remove all traces of earlier installs. @@ -248,8 +247,8 @@ source code and recompiling frequently. $ pip install --no-build-isolation --editable . ``` -- Build in parallel. Starting in v3.4.0, you can specify the number of - build jobs with the environment variable `SPACY_NUM_BUILD_JOBS`: +- Build in parallel. Starting in v3.4.0, you can specify the number of build + jobs with the environment variable `SPACY_NUM_BUILD_JOBS`: ```bash $ pip install -r requirements.txt diff --git a/website/docs/usage/processing-pipelines.md b/website/docs/usage/processing-pipelines.md index 67c88700d..b3940458b 100644 --- a/website/docs/usage/processing-pipelines.md +++ b/website/docs/usage/processing-pipelines.md @@ -364,7 +364,9 @@ nlp.enable_pipe("tagger") ``` In addition to `disable`, `spacy.load()` also accepts `enable`. If `enable` is -set, all components except for those in `enable` are disabled. +set, all components except for those in `enable` are disabled. If `enable` and +`disable` conflict (i.e. the same component is included in both), an error is +raised. ```python # Load the complete pipeline, but disable all components except for tok2vec and tagger @@ -1399,8 +1401,8 @@ Writing to a `._` attribute instead of to the `Doc` directly keeps a clearer separation and makes it easier to ensure backwards compatibility. For example, if you've implemented your own `.coref` property and spaCy claims it one day, it'll break your code. Similarly, just by looking at the code, you'll -immediately know what's built-in and what's custom – for example, -`doc.lang` is spaCy, while `doc._.language` isn't. +immediately know what's built-in and what's custom – for example, `doc.lang` is +spaCy, while `doc._.language` isn't. diff --git a/website/docs/usage/projects.md b/website/docs/usage/projects.md index 90b612358..f57578049 100644 --- a/website/docs/usage/projects.md +++ b/website/docs/usage/projects.md @@ -259,9 +259,9 @@ pipelines. > This can be used in a project command like so: > > ```yaml -> - name: "echo-path" -> script: -> - "echo ${env.ENV_PATH}" +> - name: 'echo-path' +> script: +> - 'echo ${env.ENV_PATH}' > ``` | Section | Description | @@ -643,12 +643,13 @@ locally. You can list one or more remotes in the `remotes` section of your [`project.yml`](#project-yml) by mapping a string name to the URL of the -storage. Under the hood, spaCy uses the -[`smart-open`](https://github.com/RaRe-Technologies/smart_open) library to -communicate with the remote storages, so you can use any protocol that -`smart-open` supports, including [S3](https://aws.amazon.com/s3/), -[Google Cloud Storage](https://cloud.google.com/storage), SSH and more, although -you may need to install extra dependencies to use certain protocols. +storage. Under the hood, spaCy uses +[`Pathy`](https://github.com/justindujardin/pathy) to communicate with the +remote storages, so you can use any protocol that `Pathy` supports, including +[S3](https://aws.amazon.com/s3/), +[Google Cloud Storage](https://cloud.google.com/storage), and the local +filesystem, although you may need to install extra dependencies to use certain +protocols. > #### Example > @@ -661,7 +662,6 @@ you may need to install extra dependencies to use certain protocols. remotes: default: 's3://my-spacy-bucket' local: '/mnt/scratch/cache' - stuff: 'ssh://myserver.example.com/whatever' ``` @@ -1014,54 +1014,6 @@ https://github.com/explosion/projects/blob/v3/integrations/fastapi/scripts/main. --- -### Ray {#ray} - -> #### Installation -> -> ```cli -> $ pip install -U %%SPACY_PKG_NAME[ray]%%SPACY_PKG_FLAGS -> # Check that the CLI is registered -> $ python -m spacy ray --help -> ``` - -[Ray](https://ray.io/) is a fast and simple framework for building and running -**distributed applications**. You can use Ray for parallel and distributed -training with spaCy via our lightweight -[`spacy-ray`](https://github.com/explosion/spacy-ray) extension package. If the -package is installed in the same environment as spaCy, it will automatically add -[`spacy ray`](/api/cli#ray) commands to your spaCy CLI. See the usage guide on -[parallel training](/usage/training#parallel-training) for more details on how -it works under the hood. - - - -Get started with parallel training using our project template. It trains a -simple model on a Universal Dependencies Treebank and lets you parallelize the -training with Ray. - - - -You can integrate [`spacy ray train`](/api/cli#ray-train) into your -`project.yml` just like the regular training command and pass it the config, and -optional output directory or remote storage URL and config overrides if needed. - - -```yaml -### project.yml -commands: - - name: "ray" - help: "Train a model via parallel training with Ray" - script: - - "python -m spacy ray train configs/config.cfg -o training/ --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy" - deps: - - "corpus/train.spacy" - - "corpus/dev.spacy" - outputs: - - "training/model-best" -``` - ---- - ### Weights & Biases {#wandb} [Weights & Biases](https://www.wandb.com/) is a popular platform for experiment diff --git a/website/docs/usage/rule-based-matching.md b/website/docs/usage/rule-based-matching.md index 77461c495..aa1015455 100644 --- a/website/docs/usage/rule-based-matching.md +++ b/website/docs/usage/rule-based-matching.md @@ -162,7 +162,7 @@ rule-based matching are: | Attribute | Description | | ---------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `ORTH` | The exact verbatim text of a token. ~~str~~ | -| `TEXT` 2.1 | The exact verbatim text of a token. ~~str~~ | +| `TEXT` | The exact verbatim text of a token. ~~str~~ | | `NORM` | The normalized form of the token text. ~~str~~ | | `LOWER` | The lowercase form of the token text. ~~str~~ | | `LENGTH` | The length of the token text. ~~int~~ | @@ -174,7 +174,7 @@ rule-based matching are: | `SPACY` | Token has a trailing space. ~~bool~~ | | `POS`, `TAG`, `MORPH`, `DEP`, `LEMMA`, `SHAPE` | The token's simple and extended part-of-speech tag, morphological analysis, dependency label, lemma, shape. Note that the values of these attributes are case-sensitive. For a list of available part-of-speech tags and dependency labels, see the [Annotation Specifications](/api/annotation). ~~str~~ | | `ENT_TYPE` | The token's entity label. ~~str~~ | -| `_` 2.1 | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~ | +| `_` | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~ | | `OP` | [Operator or quantifier](#quantifiers) to determine how often to match a token pattern. ~~str~~ | diff --git a/website/docs/usage/saving-loading.md b/website/docs/usage/saving-loading.md index d2b67b199..70df43336 100644 --- a/website/docs/usage/saving-loading.md +++ b/website/docs/usage/saving-loading.md @@ -306,12 +306,12 @@ pipeline component factories, language classes and other settings. To make spaCy use your entry points, your package needs to expose them and it needs to be installed in the same environment – that's it. -| Entry point | Description | -| ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [`spacy_factories`](#entry-points-components) | Group of entry points for pipeline component factories, keyed by component name. Can be used to expose custom components defined by another package. | -| [`spacy_languages`](#entry-points-languages) | Group of entry points for custom [`Language` subclasses](/usage/linguistic-features#language-data), keyed by language shortcut. | -| `spacy_lookups` 2.2 | Group of entry points for custom [`Lookups`](/api/lookups), including lemmatizer data. Used by spaCy's [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package. | -| [`spacy_displacy_colors`](#entry-points-displacy) 2.2 | Group of entry points of custom label colors for the [displaCy visualizer](/usage/visualizers#ent). The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. | +| Entry point | Description | +| ------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [`spacy_factories`](#entry-points-components) | Group of entry points for pipeline component factories, keyed by component name. Can be used to expose custom components defined by another package. | +| [`spacy_languages`](#entry-points-languages) | Group of entry points for custom [`Language` subclasses](/usage/linguistic-features#language-data), keyed by language shortcut. | +| `spacy_lookups` | Group of entry points for custom [`Lookups`](/api/lookups), including lemmatizer data. Used by spaCy's [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package. | +| [`spacy_displacy_colors`](#entry-points-displacy) | Group of entry points of custom label colors for the [displaCy visualizer](/usage/visualizers#ent). The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. | ### Custom components via entry points {#entry-points-components} diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md index 5ee148224..f119cf2ed 100644 --- a/website/docs/usage/training.md +++ b/website/docs/usage/training.md @@ -1572,77 +1572,6 @@ token-based annotations like the dependency parse or entity labels, you'll need to take care to adjust the `Example` object so its annotations match and remain valid. -## Parallel & distributed training with Ray {#parallel-training} - -> #### Installation -> -> ```cli -> $ pip install -U %%SPACY_PKG_NAME[ray]%%SPACY_PKG_FLAGS -> # Check that the CLI is registered -> $ python -m spacy ray --help -> ``` - -[Ray](https://ray.io/) is a fast and simple framework for building and running -**distributed applications**. You can use Ray to train spaCy on one or more -remote machines, potentially speeding up your training process. Parallel -training won't always be faster though – it depends on your batch size, models, -and hardware. - - - -To use Ray with spaCy, you need the -[`spacy-ray`](https://github.com/explosion/spacy-ray) package installed. -Installing the package will automatically add the `ray` command to the spaCy -CLI. - - - -The [`spacy ray train`](/api/cli#ray-train) command follows the same API as -[`spacy train`](/api/cli#train), with a few extra options to configure the Ray -setup. You can optionally set the `--address` option to point to your Ray -cluster. If it's not set, Ray will run locally. - -```cli -python -m spacy ray train config.cfg --n-workers 2 -``` - - - -Get started with parallel training using our project template. It trains a -simple model on a Universal Dependencies Treebank and lets you parallelize the -training with Ray. - - - -### How parallel training works {#parallel-training-details} - -Each worker receives a shard of the **data** and builds a copy of the **model -and optimizer** from the [`config.cfg`](#config). It also has a communication -channel to **pass gradients and parameters** to the other workers. Additionally, -each worker is given ownership of a subset of the parameter arrays. Every -parameter array is owned by exactly one worker, and the workers are given a -mapping so they know which worker owns which parameter. - -![Illustration of setup](../images/spacy-ray.svg) - -As training proceeds, every worker will be computing gradients for **all** of -the model parameters. When they compute gradients for parameters they don't own, -they'll **send them to the worker** that does own that parameter, along with a -version identifier so that the owner can decide whether to discard the gradient. -Workers use the gradients they receive and the ones they compute locally to -update the parameters they own, and then broadcast the updated array and a new -version ID to the other workers. - -This training procedure is **asynchronous** and **non-blocking**. Workers always -push their gradient increments and parameter updates, they do not have to pull -them and block on the result, so the transfers can happen in the background, -overlapped with the actual training work. The workers also do not have to stop -and wait for each other ("synchronize") at the start of each batch. This is very -useful for spaCy, because spaCy is often trained on long documents, which means -**batches can vary in size** significantly. Uneven workloads make synchronous -gradient descent inefficient, because if one batch is slow, all of the other -workers are stuck waiting for it to complete before they can continue. - ## Internal training API {#api} diff --git a/website/docs/usage/v3-4.md b/website/docs/usage/v3-4.md index 597fc3cc8..e6987e7a2 100644 --- a/website/docs/usage/v3-4.md +++ b/website/docs/usage/v3-4.md @@ -63,11 +63,11 @@ All CNN pipelines have been extended with whitespace augmentation. The English CNN pipelines have new word vectors: -| Package | Model Version | TAG | Parser LAS | NER F | -| ----------------------------------------------- | ------------- | ---: | ---------: | ----: | +| Package | Model Version | TAG | Parser LAS | NER F | +| --------------------------------------------- | ------------- | ---: | ---------: | ----: | | [`en_core_web_md`](/models/en#en_core_web_md) | v3.3.0 | 97.3 | 90.1 | 84.6 | -| [`en_core_web_md`](/models/en#en_core_web_lg) | v3.4.0 | 97.2 | 90.3 | 85.5 | -| [`en_core_web_lg`](/models/en#en_core_web_md) | v3.3.0 | 97.4 | 90.1 | 85.3 | +| [`en_core_web_md`](/models/en#en_core_web_md) | v3.4.0 | 97.2 | 90.3 | 85.5 | +| [`en_core_web_lg`](/models/en#en_core_web_lg) | v3.3.0 | 97.4 | 90.1 | 85.3 | | [`en_core_web_lg`](/models/en#en_core_web_lg) | v3.4.0 | 97.3 | 90.2 | 85.6 | ## Notes about upgrading from v3.3 {#upgrading} diff --git a/website/docs/usage/v3.md b/website/docs/usage/v3.md index 971779ed3..64f93b7c0 100644 --- a/website/docs/usage/v3.md +++ b/website/docs/usage/v3.md @@ -15,18 +15,6 @@ menu: > To help you make the transition from v2.x to v3.0, we've uploaded the old > website to [**v2.spacy.io**](https://v2.spacy.io/docs). - - -Want to make the transition from spaCy v2 to spaCy v3 as smooth as possible for -you and your organization? We're now offering commercial **migration support** -for your spaCy pipelines! We've put a lot of work into making it easy to upgrade -your existing code and training workflows – but custom projects may always need -some custom work, especially when it comes to taking advantage of the new -capabilities. -[**Details & application →**](https://form.typeform.com/to/vMs2zSjM) - - -
diff --git a/website/meta/languages.json b/website/meta/languages.json index bd1535c90..15158df79 100644 --- a/website/meta/languages.json +++ b/website/meta/languages.json @@ -562,6 +562,7 @@ "url": "https://github.com/explosion/spacy-pkuseg" } ], + "example": "这是一个用于示例的句子。", "has_examples": true } ], diff --git a/website/meta/universe.json b/website/meta/universe.json index d7c99956b..97b53e9c5 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -1,5 +1,31 @@ { "resources": [ + { + "id": "grecy", + "title": "greCy", + "slogan": "Ancient Greek pipelines for spaCy", + "description": "greCy offers state-of-the-art pipelines for ancient Greek NLP. The repository makes language models available in various sizes, some of them containing floret word vectors and a BERT transformer layer.", + "github": "jmyerston/greCy", + "code_example": [ + "import spacy", + "#After installing the grc_ud_proiel_trf wheel package from the greCy repository", + "", + "nlp = spacy.load('grc_ud_proiel_trf')", + "doc = nlp('δοκῶ μοι περὶ ὧν πυνθάνεσθε οὐκ ἀμελέτητος εἶναι.')", + "", + "for token in doc:", + " print(token.text, token.norm_, token.lemma_, token.pos_, token.tag_)" + ], + "code_language": "python", + "author": "Jacobo Myerston", + "author_links": { + "twitter": "@jcbmyrstn", + "github": "jmyerston", + "website": "https://huggingface.co/spaces/Jacobo/syntax" + }, + "category": ["pipeline", "research"], + "tags": ["ancient Greek"] + }, { "id": "spacy-cleaner", "title": "spacy-cleaner", @@ -435,37 +461,6 @@ }, "category": ["standalone"] }, - { - "id": "spikex", - "title": "SpikeX - SpaCy Pipes for Knowledge Extraction", - "slogan": "Use SpikeX to build knowledge extraction tools with almost-zero effort", - "description": "SpikeX is a collection of pipes ready to be plugged in a spaCy pipeline. It aims to help in building knowledge extraction tools with almost-zero effort.", - "github": "erre-quadro/spikex", - "pip": "spikex", - "code_example": [ - "from spacy import load as spacy_load", - "from spikex.wikigraph import load as wg_load", - "from spikex.pipes import WikiPageX", - "", - "# load a spacy model and get a doc", - "nlp = spacy_load('en_core_web_sm')", - "doc = nlp('An apple a day keeps the doctor away')", - "# load a WikiGraph", - "wg = wg_load('simplewiki_core')", - "# get a WikiPageX and extract all pages", - "wikipagex = WikiPageX(wg)", - "doc = wikipagex(doc)", - "# see all pages extracted from the doc", - "for span in doc._.wiki_spans:", - " print(span._.wiki_pages)" - ], - "category": ["pipeline", "standalone"], - "author": "Erre Quadro", - "author_links": { - "github": "erre-quadro", - "website": "https://www.errequadrosrl.com" - } - }, { "id": "spacy-dbpedia-spotlight", "title": "DBpedia Spotlight for SpaCy", @@ -531,17 +526,6 @@ "tags": ["sentiment", "textblob"], "spacy_version": 3 }, - { - "id": "spacy-ray", - "title": "spacy-ray", - "slogan": "Parallel and distributed training with spaCy and Ray", - "description": "[Ray](https://ray.io/) is a fast and simple framework for building and running **distributed applications**. This very lightweight extension package lets you use Ray for parallel and distributed training with spaCy. If `spacy-ray` is installed in the same environment as spaCy, it will automatically add `spacy ray` commands to your spaCy CLI.", - "github": "explosion/spacy-ray", - "pip": "spacy-ray", - "category": ["training"], - "author": "Explosion / Anyscale", - "thumb": "https://i.imgur.com/7so6ZpS.png" - }, { "id": "spacy-sentence-bert", "title": "spaCy - sentence-transformers", @@ -2009,17 +1993,6 @@ }, "category": ["books"] }, - { - "type": "education", - "id": "learning-path-spacy", - "title": "Learning Path: Mastering spaCy for Natural Language Processing", - "slogan": "O'Reilly, 2017", - "description": "spaCy, a fast, user-friendly library for teaching computers to understand text, simplifies NLP techniques, such as speech tagging and syntactic dependencies, so you can easily extract information, attributes, and objects from massive amounts of text to then document, measure, and analyze. This Learning Path is a hands-on introduction to using spaCy to discover insights through natural language processing. While end-to-end natural language processing solutions can be complex, you’ll learn the linguistics, algorithms, and machine learning skills to get the job done.", - "url": "https://www.safaribooksonline.com/library/view/learning-path-mastering/9781491986653/", - "thumb": "https://i.imgur.com/9MIgMAc.jpg", - "author": "Aaron Kramer", - "category": ["courses"] - }, { "type": "education", "id": "introduction-into-spacy-3",