mirror of
https://github.com/explosion/spaCy.git
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a45d89f09a
Add `initialize.before_init` and `initialize.after_init` callbacks to the config. The `initialize.before_init` callback is a place to implement one-time tokenizer customizations that are then saved with the model.
477 lines
20 KiB
Python
477 lines
20 KiB
Python
from typing import Dict, List, Union, Optional, Any, Callable, Type, Tuple
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from typing import Iterable, TypeVar, TYPE_CHECKING
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from enum import Enum
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from pydantic import BaseModel, Field, ValidationError, validator, create_model
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from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool
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from pydantic.main import ModelMetaclass
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from thinc.api import Optimizer, ConfigValidationError
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from thinc.config import Promise
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from collections import defaultdict
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import inspect
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from .attrs import NAMES
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from .lookups import Lookups
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from .util import is_cython_func
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if TYPE_CHECKING:
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# This lets us add type hints for mypy etc. without causing circular imports
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from .language import Language # noqa: F401
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from .training import Example # noqa: F401
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# fmt: off
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ItemT = TypeVar("ItemT")
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Batcher = Union[Callable[[Iterable[ItemT]], Iterable[List[ItemT]]], Promise]
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Reader = Union[Callable[["Language", str], Iterable["Example"]], Promise]
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Logger = Union[Callable[["Language"], Tuple[Callable[[Dict[str, Any]], None], Callable]], Promise]
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# fmt: on
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def validate(schema: Type[BaseModel], obj: Dict[str, Any]) -> List[str]:
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"""Validate data against a given pydantic schema.
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obj (Dict[str, Any]): JSON-serializable data to validate.
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schema (pydantic.BaseModel): The schema to validate against.
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RETURNS (List[str]): A list of error messages, if available.
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"""
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try:
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schema(**obj)
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return []
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except ValidationError as e:
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errors = e.errors()
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data = defaultdict(list)
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for error in errors:
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err_loc = " -> ".join([str(p) for p in error.get("loc", [])])
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data[err_loc].append(error.get("msg"))
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return [f"[{loc}] {', '.join(msg)}" for loc, msg in data.items()]
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# Initialization
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class ArgSchemaConfig:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ArgSchemaConfigExtra:
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extra = "forbid"
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arbitrary_types_allowed = True
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def get_arg_model(
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func: Callable,
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*,
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exclude: Iterable[str] = tuple(),
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name: str = "ArgModel",
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strict: bool = True,
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) -> ModelMetaclass:
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"""Generate a pydantic model for function arguments.
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func (Callable): The function to generate the schema for.
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exclude (Iterable[str]): Parameter names to ignore.
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name (str): Name of created model class.
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strict (bool): Don't allow extra arguments if no variable keyword arguments
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are allowed on the function.
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RETURNS (ModelMetaclass): A pydantic model.
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"""
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sig_args = {}
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try:
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sig = inspect.signature(func)
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except ValueError:
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# Typically happens if the method is part of a Cython module without
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# binding=True. Here we just use an empty model that allows everything.
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return create_model(name, __config__=ArgSchemaConfigExtra)
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has_variable = False
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for param in sig.parameters.values():
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if param.name in exclude:
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continue
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if param.kind == param.VAR_KEYWORD:
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# The function allows variable keyword arguments so we shouldn't
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# include **kwargs etc. in the schema and switch to non-strict
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# mode and pass through all other values
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has_variable = True
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continue
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# If no annotation is specified assume it's anything
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annotation = param.annotation if param.annotation != param.empty else Any
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# If no default value is specified assume that it's required. Cython
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# functions/methods will have param.empty for default value None so we
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# need to treat them differently
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default_empty = None if is_cython_func(func) else ...
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default = param.default if param.default != param.empty else default_empty
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sig_args[param.name] = (annotation, default)
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is_strict = strict and not has_variable
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sig_args["__config__"] = ArgSchemaConfig if is_strict else ArgSchemaConfigExtra
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return create_model(name, **sig_args)
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def validate_init_settings(
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func: Callable,
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settings: Dict[str, Any],
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*,
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section: Optional[str] = None,
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name: str = "",
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exclude: Iterable[str] = ("get_examples", "nlp"),
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) -> Dict[str, Any]:
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"""Validate initialization settings against the expected arguments in
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the method signature. Will parse values if possible (e.g. int to string)
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and return the updated settings dict. Will raise a ConfigValidationError
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if types don't match or required values are missing.
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func (Callable): The initialize method of a given component etc.
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settings (Dict[str, Any]): The settings from the respective [initialize] block.
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section (str): Initialize section, for error message.
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name (str): Name of the block in the section.
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exclude (Iterable[str]): Parameter names to exclude from schema.
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RETURNS (Dict[str, Any]): The validated settings.
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"""
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schema = get_arg_model(func, exclude=exclude, name="InitArgModel")
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try:
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return schema(**settings).dict()
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except ValidationError as e:
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block = "initialize" if not section else f"initialize.{section}"
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title = f"Error validating initialization settings in [{block}]"
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raise ConfigValidationError(
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title=title, errors=e.errors(), config=settings, parent=name
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) from None
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# Matcher token patterns
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def validate_token_pattern(obj: list) -> List[str]:
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# Try to convert non-string keys (e.g. {ORTH: "foo"} -> {"ORTH": "foo"})
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get_key = lambda k: NAMES[k] if isinstance(k, int) and k < len(NAMES) else k
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if isinstance(obj, list):
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converted = []
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for pattern in obj:
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if isinstance(pattern, dict):
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pattern = {get_key(k): v for k, v in pattern.items()}
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converted.append(pattern)
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obj = converted
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return validate(TokenPatternSchema, {"pattern": obj})
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class TokenPatternString(BaseModel):
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REGEX: Optional[StrictStr] = Field(None, alias="regex")
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IN: Optional[List[StrictStr]] = Field(None, alias="in")
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NOT_IN: Optional[List[StrictStr]] = Field(None, alias="not_in")
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IS_SUBSET: Optional[List[StrictStr]] = Field(None, alias="is_subset")
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IS_SUPERSET: Optional[List[StrictStr]] = Field(None, alias="is_superset")
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class Config:
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extra = "forbid"
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allow_population_by_field_name = True # allow alias and field name
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@validator("*", pre=True, each_item=True, allow_reuse=True)
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def raise_for_none(cls, v):
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if v is None:
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raise ValueError("None / null is not allowed")
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return v
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class TokenPatternNumber(BaseModel):
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REGEX: Optional[StrictStr] = Field(None, alias="regex")
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IN: Optional[List[StrictInt]] = Field(None, alias="in")
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NOT_IN: Optional[List[StrictInt]] = Field(None, alias="not_in")
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ISSUBSET: Optional[List[StrictInt]] = Field(None, alias="issubset")
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ISSUPERSET: Optional[List[StrictInt]] = Field(None, alias="issuperset")
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EQ: Union[StrictInt, StrictFloat] = Field(None, alias="==")
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NEQ: Union[StrictInt, StrictFloat] = Field(None, alias="!=")
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GEQ: Union[StrictInt, StrictFloat] = Field(None, alias=">=")
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LEQ: Union[StrictInt, StrictFloat] = Field(None, alias="<=")
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GT: Union[StrictInt, StrictFloat] = Field(None, alias=">")
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LT: Union[StrictInt, StrictFloat] = Field(None, alias="<")
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class Config:
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extra = "forbid"
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allow_population_by_field_name = True # allow alias and field name
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@validator("*", pre=True, each_item=True, allow_reuse=True)
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def raise_for_none(cls, v):
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if v is None:
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raise ValueError("None / null is not allowed")
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return v
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class TokenPatternOperator(str, Enum):
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plus: StrictStr = "+"
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start: StrictStr = "*"
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question: StrictStr = "?"
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exclamation: StrictStr = "!"
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StringValue = Union[TokenPatternString, StrictStr]
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NumberValue = Union[TokenPatternNumber, StrictInt, StrictFloat]
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UnderscoreValue = Union[
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TokenPatternString, TokenPatternNumber, str, int, float, list, bool
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]
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class TokenPattern(BaseModel):
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orth: Optional[StringValue] = None
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text: Optional[StringValue] = None
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lower: Optional[StringValue] = None
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pos: Optional[StringValue] = None
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tag: Optional[StringValue] = None
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morph: Optional[StringValue] = None
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dep: Optional[StringValue] = None
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lemma: Optional[StringValue] = None
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shape: Optional[StringValue] = None
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ent_type: Optional[StringValue] = None
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norm: Optional[StringValue] = None
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length: Optional[NumberValue] = None
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spacy: Optional[StrictBool] = None
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is_alpha: Optional[StrictBool] = None
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is_ascii: Optional[StrictBool] = None
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is_digit: Optional[StrictBool] = None
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is_lower: Optional[StrictBool] = None
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is_upper: Optional[StrictBool] = None
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is_title: Optional[StrictBool] = None
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is_punct: Optional[StrictBool] = None
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is_space: Optional[StrictBool] = None
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is_bracket: Optional[StrictBool] = None
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is_quote: Optional[StrictBool] = None
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is_left_punct: Optional[StrictBool] = None
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is_right_punct: Optional[StrictBool] = None
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is_currency: Optional[StrictBool] = None
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is_stop: Optional[StrictBool] = None
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is_sent_start: Optional[StrictBool] = None
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sent_start: Optional[StrictBool] = None
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like_num: Optional[StrictBool] = None
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like_url: Optional[StrictBool] = None
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like_email: Optional[StrictBool] = None
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op: Optional[TokenPatternOperator] = None
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underscore: Optional[Dict[StrictStr, UnderscoreValue]] = Field(None, alias="_")
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class Config:
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extra = "forbid"
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allow_population_by_field_name = True
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alias_generator = lambda value: value.upper()
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@validator("*", pre=True, allow_reuse=True)
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def raise_for_none(cls, v):
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if v is None:
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raise ValueError("None / null is not allowed")
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return v
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class TokenPatternSchema(BaseModel):
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pattern: List[TokenPattern] = Field(..., min_items=1)
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class Config:
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extra = "forbid"
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# Model meta
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class ModelMetaSchema(BaseModel):
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# fmt: off
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lang: StrictStr = Field(..., title="Two-letter language code, e.g. 'en'")
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name: StrictStr = Field(..., title="Model name")
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version: StrictStr = Field(..., title="Model version")
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spacy_version: StrictStr = Field("", title="Compatible spaCy version identifier")
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parent_package: StrictStr = Field("spacy", title="Name of parent spaCy package, e.g. spacy or spacy-nightly")
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requirements: List[StrictStr] = Field([], title="Additional Python package dependencies, used for the Python package setup")
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pipeline: List[StrictStr] = Field([], title="Names of pipeline components")
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description: StrictStr = Field("", title="Model description")
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license: StrictStr = Field("", title="Model license")
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author: StrictStr = Field("", title="Model author name")
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email: StrictStr = Field("", title="Model author email")
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url: StrictStr = Field("", title="Model author URL")
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sources: Optional[Union[List[StrictStr], List[Dict[str, str]]]] = Field(None, title="Training data sources")
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vectors: Dict[str, Any] = Field({}, title="Included word vectors")
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labels: Dict[str, List[str]] = Field({}, title="Component labels, keyed by component name")
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performance: Dict[str, Any] = Field({}, title="Accuracy and speed numbers")
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spacy_git_version: StrictStr = Field("", title="Commit of spaCy version used")
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# fmt: on
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# Config schema
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# We're not setting any defaults here (which is too messy) and are making all
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# fields required, so we can raise validation errors for missing values. To
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# provide a default, we include a separate .cfg file with all values and
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# check that against this schema in the test suite to make sure it's always
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# up to date.
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class ConfigSchemaTraining(BaseModel):
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# fmt: off
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dev_corpus: StrictStr = Field(..., title="Path in the config to the dev data")
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train_corpus: StrictStr = Field(..., title="Path in the config to the training data")
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batcher: Batcher = Field(..., title="Batcher for the training data")
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dropout: StrictFloat = Field(..., title="Dropout rate")
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patience: StrictInt = Field(..., title="How many steps to continue without improvement in evaluation score")
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max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
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max_steps: StrictInt = Field(..., title="Maximum number of update steps to train for")
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eval_frequency: StrictInt = Field(..., title="How often to evaluate during training (steps)")
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seed: Optional[StrictInt] = Field(..., title="Random seed")
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gpu_allocator: Optional[StrictStr] = Field(..., title="Memory allocator when running on GPU")
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accumulate_gradient: StrictInt = Field(..., title="Whether to divide the batch up into substeps")
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score_weights: Dict[StrictStr, Optional[Union[StrictFloat, StrictInt]]] = Field(..., title="Scores to report and their weights for selecting final model")
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optimizer: Optimizer = Field(..., title="The optimizer to use")
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logger: Logger = Field(..., title="The logger to track training progress")
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frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
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before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
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# fmt: on
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class Config:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ConfigSchemaNlp(BaseModel):
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# fmt: off
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lang: StrictStr = Field(..., title="The base language to use")
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pipeline: List[StrictStr] = Field(..., title="The pipeline component names in order")
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disabled: List[StrictStr] = Field(..., title="Pipeline components to disable by default")
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tokenizer: Callable = Field(..., title="The tokenizer to use")
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before_creation: Optional[Callable[[Type["Language"]], Type["Language"]]] = Field(..., title="Optional callback to modify Language class before initialization")
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after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed")
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after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed")
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batch_size: Optional[int] = Field(..., title="Default batch size")
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# fmt: on
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class Config:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ConfigSchemaPretrainEmpty(BaseModel):
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class Config:
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extra = "forbid"
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class ConfigSchemaPretrain(BaseModel):
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# fmt: off
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max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
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dropout: StrictFloat = Field(..., title="Dropout rate")
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n_save_every: Optional[StrictInt] = Field(..., title="Saving frequency")
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optimizer: Optimizer = Field(..., title="The optimizer to use")
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corpus: StrictStr = Field(..., title="Path in the config to the training data")
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batcher: Batcher = Field(..., title="Batcher for the training data")
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component: str = Field(..., title="Component to find the layer to pretrain")
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layer: str = Field(..., title="Layer to pretrain. Whole model if empty.")
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objective: Callable[["Vocab", "Model"], "Model"] = Field(..., title="A function that creates the pretraining objective.")
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# fmt: on
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class Config:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ConfigSchemaInit(BaseModel):
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# fmt: off
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vocab_data: Optional[StrictStr] = Field(..., title="Path to JSON-formatted vocabulary file")
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lookups: Optional[Lookups] = Field(..., title="Vocabulary lookups, e.g. lexeme normalization")
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vectors: Optional[StrictStr] = Field(..., title="Path to vectors")
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init_tok2vec: Optional[StrictStr] = Field(..., title="Path to pretrained tok2vec weights")
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tokenizer: Dict[StrictStr, Any] = Field(..., help="Arguments to be passed into Tokenizer.initialize")
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components: Dict[StrictStr, Dict[StrictStr, Any]] = Field(..., help="Arguments for TrainablePipe.initialize methods of pipeline components, keyed by component")
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before_init: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object before initialization")
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after_init: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after initialization")
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# fmt: on
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class Config:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ConfigSchema(BaseModel):
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training: ConfigSchemaTraining
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nlp: ConfigSchemaNlp
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pretraining: Union[ConfigSchemaPretrain, ConfigSchemaPretrainEmpty] = {}
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components: Dict[str, Dict[str, Any]]
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corpora: Dict[str, Reader]
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initialize: ConfigSchemaInit
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class Config:
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extra = "allow"
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arbitrary_types_allowed = True
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CONFIG_SCHEMAS = {
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"nlp": ConfigSchemaNlp,
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"training": ConfigSchemaTraining,
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"pretraining": ConfigSchemaPretrain,
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"initialize": ConfigSchemaInit,
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}
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# Project config Schema
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class ProjectConfigAssetGitItem(BaseModel):
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# fmt: off
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repo: StrictStr = Field(..., title="URL of Git repo to download from")
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path: StrictStr = Field(..., title="File path or sub-directory to download (used for sparse checkout)")
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branch: StrictStr = Field("master", title="Branch to clone from")
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# fmt: on
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class ProjectConfigAssetURL(BaseModel):
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# fmt: off
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dest: StrictStr = Field(..., title="Destination of downloaded asset")
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url: Optional[StrictStr] = Field(None, title="URL of asset")
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checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
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description: StrictStr = Field("", title="Description of asset")
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# fmt: on
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class ProjectConfigAssetGit(BaseModel):
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# fmt: off
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git: ProjectConfigAssetGitItem = Field(..., title="Git repo information")
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checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
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description: Optional[StrictStr] = Field(None, title="Description of asset")
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# fmt: on
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class ProjectConfigCommand(BaseModel):
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# fmt: off
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name: StrictStr = Field(..., title="Name of command")
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help: Optional[StrictStr] = Field(None, title="Command description")
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script: List[StrictStr] = Field([], title="List of CLI commands to run, in order")
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deps: List[StrictStr] = Field([], title="File dependencies required by this command")
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outputs: List[StrictStr] = Field([], title="Outputs produced by this command")
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outputs_no_cache: List[StrictStr] = Field([], title="Outputs not tracked by DVC (DVC only)")
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no_skip: bool = Field(False, title="Never skip this command, even if nothing changed")
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# fmt: on
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class Config:
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title = "A single named command specified in a project config"
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extra = "forbid"
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class ProjectConfigSchema(BaseModel):
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# fmt: off
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vars: Dict[StrictStr, Any] = Field({}, title="Optional variables to substitute in commands")
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assets: List[Union[ProjectConfigAssetURL, ProjectConfigAssetGit]] = Field([], title="Data assets")
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workflows: Dict[StrictStr, List[StrictStr]] = Field({}, title="Named workflows, mapped to list of project commands to run in order")
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commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
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title: Optional[str] = Field(None, title="Project title")
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spacy_version: Optional[StrictStr] = Field(None, title="spaCy version range that the project is compatible with")
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# fmt: on
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class Config:
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title = "Schema for project configuration file"
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# Recommendations for init config workflows
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class RecommendationTrfItem(BaseModel):
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name: str
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size_factor: int
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class RecommendationTrf(BaseModel):
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efficiency: RecommendationTrfItem
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accuracy: RecommendationTrfItem
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class RecommendationSchema(BaseModel):
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word_vectors: Optional[str] = None
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transformer: Optional[RecommendationTrf] = None
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has_letters: bool = True
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