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Add more info to debug config
This commit is contained in:
Ines Montani
parent
e1e1760fd6
commit
ff4175e839
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@ -7,7 +7,7 @@ ml_datasets>=0.1.1
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murmurhash>=0.28.0,<1.1.0
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wasabi>=0.8.0,<1.1.0
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srsly>=2.1.0,<3.0.0
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catalogue>=0.0.7,<1.1.0
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catalogue>=2.0.1,<2.1.0
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typer>=0.3.0,<0.4.0
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pathy
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# Third party dependencies
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@ -44,7 +44,7 @@ install_requires =
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blis>=0.4.0,<0.5.0
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wasabi>=0.8.0,<1.1.0
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srsly>=2.1.0,<3.0.0
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catalogue>=0.0.7,<1.1.0
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catalogue>=2.0.1,<2.1.0
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typer>=0.3.0,<0.4.0
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pathy
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# Third-party dependencies
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@ -11,6 +11,7 @@ from .profile import profile # noqa: F401
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from .train import train_cli # noqa: F401
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from .pretrain import pretrain # noqa: F401
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from .debug_data import debug_data # noqa: F401
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from .debug_config import debug_config # noqa: F401
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from .debug_model import debug_model # noqa: F401
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from .evaluate import evaluate # noqa: F401
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from .convert import convert # noqa: F401
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93
spacy/cli/debug_config.py
Normal file
93
spacy/cli/debug_config.py
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@ -0,0 +1,93 @@
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from typing import Optional, Dict, Any, Union, List
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from pathlib import Path
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from wasabi import msg, table
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from thinc.api import Config
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from thinc.config import VARIABLE_RE
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import typer
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from ._util import Arg, Opt, show_validation_error, parse_config_overrides
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from ._util import import_code, debug_cli
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from .. import util
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@debug_cli.command(
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"config",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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)
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def debug_config_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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show_funcs: bool = Opt(False, "--show-functions", "-F", help="Show an overview of all registered functions used in the config and where they come from (modules, files etc.)"),
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show_vars: bool = Opt(False, "--show-variables", "-V", help="Show an overview of all variables referenced in the config and their values. This will also reflect variables overwritten on the CLI.")
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# fmt: on
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):
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"""Debug a config.cfg file and show validation errors. The command will
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create all objects in the tree and validate them. Note that some config
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validation errors are blocking and will prevent the rest of the config from
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being resolved. This means that you may not see all validation errors at
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once and some issues are only shown once previous errors have been fixed.
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Similar as with the 'train' command, you can override settings from the config
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as command line options. For instance, --training.batch_size 128 overrides
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the value of "batch_size" in the block "[training]".
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"""
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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debug_config(
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config_path, overrides=overrides, show_funcs=show_funcs, show_vars=show_vars
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)
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def debug_config(
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config_path: Path,
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*,
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overrides: Dict[str, Any] = {},
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show_funcs: bool = False,
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show_vars: bool = False,
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):
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msg.divider("Config validation")
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides)
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nlp, _ = util.load_model_from_config(config)
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msg.good("Config is valid")
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if show_vars:
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variables = get_variables(config)
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msg.divider(f"Variables ({len(variables)})")
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head = ("Variable", "Value")
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msg.table(variables, header=head, divider=True, widths=(41, 34), spacing=2)
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if show_funcs:
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funcs = get_registered_funcs(config)
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msg.divider(f"Registered functions ({len(funcs)})")
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for func in funcs:
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func_data = {
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"Registry": f"@{func['registry']}",
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"Name": func["name"],
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"Module": func["module"],
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"File": f"{func['file']} (line {func['line_no']})",
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}
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msg.info(f"[{func['path']}]")
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print(table(func_data).strip())
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def get_registered_funcs(config: Config) -> List[Dict[str, Optional[Union[str, int]]]]:
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result = []
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for key, value in util.walk_dict(config):
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if not key[-1].startswith("@"):
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continue
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# We have a reference to a registered function
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reg_name = key[-1][1:]
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registry = getattr(util.registry, reg_name)
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path = ".".join(key[:-1])
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info = registry.find(value)
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result.append({"name": value, "registry": reg_name, "path": path, **info})
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return result
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def get_variables(config: Config) -> Dict[str, Any]:
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result = {}
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for variable in sorted(set(VARIABLE_RE.findall(config.to_str()))):
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path = variable[2:-1].replace(":", ".")
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value = util.dot_to_object(config, path)
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result[variable] = repr(value)
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return result
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@ -23,34 +23,6 @@ BLANK_MODEL_MIN_THRESHOLD = 100
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BLANK_MODEL_THRESHOLD = 2000
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@debug_cli.command(
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"config",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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)
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def debug_config_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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# fmt: on
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):
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"""Debug a config.cfg file and show validation errors. The command will
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create all objects in the tree and validate them. Note that some config
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validation errors are blocking and will prevent the rest of the config from
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being resolved. This means that you may not see all validation errors at
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once and some issues are only shown once previous errors have been fixed.
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Similar as with the 'train' command, you can override settings from the config
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as command line options. For instance, --training.batch_size 128 overrides
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the value of "batch_size" in the block "[training]".
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"""
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides)
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nlp, _ = util.load_model_from_config(config)
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msg.good("Original config is valid")
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@debug_cli.command(
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"data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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)
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@ -246,19 +246,19 @@ some config validation errors are blocking and will prevent the rest of the
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config from being resolved. This means that you may not see all validation
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errors at once and some issues are only shown once previous errors have been
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fixed. To auto-fill a partial config and save the result, you can use the
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[`init fillconfig`](/api/cli#init-fill-config) command.
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[`init fill-config`](/api/cli#init-fill-config) command.
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```cli
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$ python -m spacy debug config [config_path] [--code_path] [overrides]
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$ python -m spacy debug config [config_path] [--code-path] [--show-functions] [--show-variables] [overrides]
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```
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> #### Example
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>
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> ```cli
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> $ python -m spacy debug config ./config.cfg
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> $ python -m spacy debug config config.cfg
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> ```
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<Accordion title="Example output" spaced>
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<Accordion title="Example output (validation error)">
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```
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✘ Config validation error
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@ -277,10 +277,124 @@ python -m spacy init fill-config tmp/starter-config_invalid.cfg --base tmp/start
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</Accordion>
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<Accordion title="Example output (valid config and all options)" spaced>
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```cli
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$ python -m spacy debug config ./config.cfg --show-functions --show-variables
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```
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```
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============================= Config validation =============================
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✔ Config is valid
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=============================== Variables (6) ===============================
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Variable Value
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----------------------------------------- ----------------------------------
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${components.tok2vec.model.encode.width} 96
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${paths.dev} 'hello'
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${paths.init_tok2vec} None
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${paths.raw} None
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${paths.train} ''
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${system.seed} 0
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========================= Registered functions (17) =========================
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ℹ [nlp.tokenizer]
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Registry @tokenizers
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Name spacy.Tokenizer.v1
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Module spacy.language
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File /path/to/spacy/language.py (line 64)
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ℹ [components.ner.model]
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Registry @architectures
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Name spacy.TransitionBasedParser.v1
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Module spacy.ml.models.parser
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File /path/to/spacy/ml/models/parser.py (line 11)
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ℹ [components.ner.model.tok2vec]
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Registry @architectures
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Name spacy.Tok2VecListener.v1
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Module spacy.ml.models.tok2vec
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File /path/to/spacy/ml/models/tok2vec.py (line 16)
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ℹ [components.parser.model]
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Registry @architectures
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Name spacy.TransitionBasedParser.v1
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Module spacy.ml.models.parser
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File /path/to/spacy/ml/models/parser.py (line 11)
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ℹ [components.parser.model.tok2vec]
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Registry @architectures
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Name spacy.Tok2VecListener.v1
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Module spacy.ml.models.tok2vec
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File /path/to/spacy/ml/models/tok2vec.py (line 16)
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ℹ [components.tagger.model]
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Registry @architectures
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Name spacy.Tagger.v1
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Module spacy.ml.models.tagger
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File /path/to/spacy/ml/models/tagger.py (line 9)
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ℹ [components.tagger.model.tok2vec]
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Registry @architectures
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Name spacy.Tok2VecListener.v1
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Module spacy.ml.models.tok2vec
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File /path/to/spacy/ml/models/tok2vec.py (line 16)
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ℹ [components.tok2vec.model]
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Registry @architectures
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Name spacy.Tok2Vec.v1
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Module spacy.ml.models.tok2vec
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File /path/to/spacy/ml/models/tok2vec.py (line 72)
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ℹ [components.tok2vec.model.embed]
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Registry @architectures
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Name spacy.MultiHashEmbed.v1
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Module spacy.ml.models.tok2vec
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File /path/to/spacy/ml/models/tok2vec.py (line 93)
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ℹ [components.tok2vec.model.encode]
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Registry @architectures
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Name spacy.MaxoutWindowEncoder.v1
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Module spacy.ml.models.tok2vec
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File /path/to/spacy/ml/models/tok2vec.py (line 207)
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ℹ [training.logger]
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Registry @loggers
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Name spacy.ConsoleLogger.v1
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Module spacy.gold.loggers
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File /path/to/spacy/gold/loggers.py (line 8)
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ℹ [training.batcher]
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Registry @batchers
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Name batch_by_words.v1
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Module spacy.gold.batchers
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File /path/to/spacy/gold/batchers.py (line 49)
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ℹ [training.batcher.size]
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Registry @schedules
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Name compounding.v1
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Module thinc.schedules
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File /Users/ines/Repos/explosion/thinc/thinc/schedules.py (line 43)
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ℹ [training.dev_corpus]
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Registry @readers
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Name spacy.Corpus.v1
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Module spacy.gold.corpus
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File /path/to/spacy/gold/corpus.py (line 18)
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ℹ [training.optimizer]
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Registry @optimizers
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Name Adam.v1
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Module thinc.optimizers
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File /Users/ines/Repos/explosion/thinc/thinc/optimizers.py (line 58)
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ℹ [training.optimizer.learn_rate]
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Registry @schedules
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Name warmup_linear.v1
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Module thinc.schedules
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File /Users/ines/Repos/explosion/thinc/thinc/schedules.py (line 91)
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ℹ [training.train_corpus]
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Registry @readers
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Name spacy.Corpus.v1
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Module spacy.gold.corpus
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File /path/to/spacy/gold/corpus.py (line 18)
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```
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</Accordion>
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| Name | Description |
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| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
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| `--code_path`, `-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)~~ |
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| `--code-path`, `-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)~~ |
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| `--show-functions`, `-F` | Show an overview of all registered function blocks used in the config and where those functions come from, including the module name, Python file and line number. ~~bool (flag)~~ |
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| `--show-variables`, `-V` | Show an overview of all variables referenced in the config, e.g. `${paths.train}` and their values that will be used. This also reflects any config overrides provided on the CLI, e.g. `--paths.train /path`. ~~bool (flag)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| 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)~~ |
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| **PRINTS** | Config validation errors, if available. |
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