medcat.components.addons.relation_extraction.base_component =========================================================== .. py:module:: medcat.components.addons.relation_extraction.base_component Attributes ---------- .. autoapisummary:: medcat.components.addons.relation_extraction.base_component.logger Classes ------- .. autoapisummary:: medcat.components.addons.relation_extraction.base_component.ConfigRelCAT medcat.components.addons.relation_extraction.base_component.RelExtrBaseModel medcat.components.addons.relation_extraction.base_component.Pad_Sequence medcat.components.addons.relation_extraction.base_component.BaseTokenizerWrapper medcat.components.addons.relation_extraction.base_component.RelExtrBaseConfig medcat.components.addons.relation_extraction.base_component.RelExtrBaseComponent Functions --------- .. autoapisummary:: medcat.components.addons.relation_extraction.base_component.serialise medcat.components.addons.relation_extraction.base_component.load_state medcat.components.addons.relation_extraction.base_component.save_state Module Contents --------------- .. py:class:: ConfigRelCAT(/, **data) Bases: :py:obj:`medcat.config.config.ComponentConfig` The RelCAT part of the config .. py:attribute:: general :type: General .. py:attribute:: model :type: Model .. py:attribute:: train :type: Train .. py:class:: Config .. py:attribute:: extra :value: 'allow' .. py:attribute:: validate_assignment :value: True .. py:method:: load(load_path = './') :classmethod: Load the config from a file. :param load_path: Path to RelCAT config. Defaults to "./". :type load_path: str :Returns: **ConfigRelCAT** -- The loaded config. .. py:attribute:: comp_name :type: str :value: 'default' The name of the component. If a custom implementation is required, it needs to be registered using `medcat.components.types.register_core_component( , , ) By default, only the 'default' component is registered. .. py:attribute:: _is_dirty :type: bool :value: False .. py:method:: __setattr__(name, value) .. py:property:: is_dirty :type: bool .. py:method:: mark_clean() .. py:method:: get_strategy() .. py:method:: get_init_attrs() :classmethod: .. py:method:: ignore_attrs() :classmethod: .. py:method:: include_properties() :classmethod: .. py:method:: merge_config(other) Merge this config with another config's (partial) model dump. The exepctation is that the `other` dict is a partial model dump. Values specified there are overwritten into the current config. Values not specified there are left intact. The `other` config can have keys/values that do not exist in the config or sub-config. And they will be added where possible. :param other: The model dump :type other: dict :raises IncorrectConfigValues: If unable to set the attribute, trying to set incorrect value, or trying to set sub-config values in an incorrect format (non-dict). .. py:attribute:: model_config :type: ClassVar[pydantic.config.ConfigDict] Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. .. py:attribute:: model_fields :type: ClassVar[Dict[str, pydantic.fields.FieldInfo]] Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. This replaces `Model.__fields__` from Pydantic V1. .. py:attribute:: model_computed_fields :type: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects. .. py:attribute:: __class_vars__ :type: ClassVar[set[str]] The names of the class variables defined on the model. .. py:attribute:: __private_attributes__ :type: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]] Metadata about the private attributes of the model. .. py:attribute:: __signature__ :type: ClassVar[inspect.Signature] The synthesized `__init__` [`Signature`][inspect.Signature] of the model. .. py:attribute:: __pydantic_complete__ :type: ClassVar[bool] :value: False Whether model building is completed, or if there are still undefined fields. .. py:attribute:: __pydantic_core_schema__ :type: ClassVar[pydantic_core.CoreSchema] The core schema of the model. .. py:attribute:: __pydantic_custom_init__ :type: ClassVar[bool] Whether the model has a custom `__init__` method. .. py:attribute:: __pydantic_decorators__ :type: ClassVar[pydantic._internal._decorators.DecoratorInfos] Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. .. py:attribute:: __pydantic_generic_metadata__ :type: ClassVar[pydantic._internal._generics.PydanticGenericMetadata] Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these. .. py:attribute:: __pydantic_parent_namespace__ :type: ClassVar[Dict[str, Any] | None] :value: None Parent namespace of the model, used for automatic rebuilding of models. .. py:attribute:: __pydantic_post_init__ :type: ClassVar[None | Literal['model_post_init']] The name of the post-init method for the model, if defined. .. py:attribute:: __pydantic_root_model__ :type: ClassVar[bool] :value: False Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. .. py:attribute:: __pydantic_serializer__ :type: ClassVar[pydantic_core.SchemaSerializer] The `pydantic-core` `SchemaSerializer` used to dump instances of the model. .. py:attribute:: __pydantic_validator__ :type: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator] The `pydantic-core` `SchemaValidator` used to validate instances of the model. .. py:attribute:: __pydantic_extra__ :type: dict[str, Any] | None A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. .. py:attribute:: __pydantic_fields_set__ :type: set[str] The names of fields explicitly set during instantiation. .. py:attribute:: __pydantic_private__ :type: dict[str, Any] | None Values of private attributes set on the model instance. .. py:attribute:: __slots__ :value: ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__') .. py:method:: __init__(/, **data) Create a new model by parsing and validating input data from keyword arguments. Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model. `self` is explicitly positional-only to allow `self` as a field name. .. py:property:: model_extra :type: dict[str, Any] | None Get extra fields set during validation. :Returns: **A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.** .. py:property:: model_fields_set :type: set[str] Returns the set of fields that have been explicitly set on this model instance. :Returns: **A set of strings representing the fields that have been set,** -- i.e. that were not filled from defaults. .. py:method:: model_construct(_fields_set = None, **values) :classmethod: Creates a new instance of the `Model` class with validated data. Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data. Default values are respected, but no other validation is performed. !!! note `model_construct()` generally respects the `model_config.extra` setting on the provided model. That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in an error if extra values are passed, but they will be ignored. :param _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the `values` argument will be used. :param values: Trusted or pre-validated data dictionary. :Returns: **A new instance of the `Model` class with validated data.** .. py:method:: model_copy(*, update = None, deep = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy Returns a copy of the model. :param update: Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. :param deep: Set to `True` to make a deep copy of the model. :Returns: **New model instance.** .. py:method:: model_dump(*, mode = 'python', include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. :param mode: The mode in which `to_python` should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. :param include: A set of fields to include in the output. :param exclude: A set of fields to exclude from the output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to use the field's alias in the dictionary key if defined. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A dictionary representation of the model.** .. py:method:: model_dump_json(*, indent = None, include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json Generates a JSON representation of the model using Pydantic's `to_json` method. :param indent: Indentation to use in the JSON output. If None is passed, the output will be compact. :param include: Field(s) to include in the JSON output. :param exclude: Field(s) to exclude from the JSON output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to serialize using field aliases. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A JSON string representation of the model.** .. py:method:: model_json_schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, schema_generator = GenerateJsonSchema, mode = 'validation') :classmethod: Generates a JSON schema for a model class. :param by_alias: Whether to use attribute aliases or not. :param ref_template: The reference template. :param schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications :param mode: The mode in which to generate the schema. :Returns: **The JSON schema for the given model class.** .. py:method:: model_parametrized_name(params) :classmethod: Compute the class name for parametrizations of generic classes. This method can be overridden to achieve a custom naming scheme for generic BaseModels. :param params: Tuple of types of the class. Given a generic class `Model` with 2 type variables and a concrete model `Model[str, int]`, the value `(str, int)` would be passed to `params`. :Returns: **String representing the new class where `params` are passed to `cls` as type variables.** :raises TypeError: Raised when trying to generate concrete names for non-generic models. .. py:method:: model_post_init(__context) Override this method to perform additional initialization after `__init__` and `model_construct`. This is useful if you want to do some validation that requires the entire model to be initialized. .. py:method:: model_rebuild(*, force = False, raise_errors = True, _parent_namespace_depth = 2, _types_namespace = None) :classmethod: Try to rebuild the pydantic-core schema for the model. This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails. :param force: Whether to force the rebuilding of the model schema, defaults to `False`. :param raise_errors: Whether to raise errors, defaults to `True`. :param _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. :param _types_namespace: The types namespace, defaults to `None`. :Returns: * **Returns `None` if the schema is already "complete" and rebuilding was not required.** * **If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.** .. py:method:: model_validate(obj, *, strict = None, from_attributes = None, context = None) :classmethod: Validate a pydantic model instance. :param obj: The object to validate. :param strict: Whether to enforce types strictly. :param from_attributes: Whether to extract data from object attributes. :param context: Additional context to pass to the validator. :raises ValidationError: If the object could not be validated. :Returns: **The validated model instance.** .. py:method:: model_validate_json(json_data, *, strict = None, context = None) :classmethod: Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. :param json_data: The JSON data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** :raises ValidationError: If `json_data` is not a JSON string or the object could not be validated. .. py:method:: model_validate_strings(obj, *, strict = None, context = None) :classmethod: Validate the given object with string data against the Pydantic model. :param obj: The object containing string data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** .. py:method:: __get_pydantic_core_schema__(source, handler, /) :classmethod: Hook into generating the model's CoreSchema. :param source: The class we are generating a schema for. This will generally be the same as the `cls` argument if this is a classmethod. :param handler: A callable that calls into Pydantic's internal CoreSchema generation logic. :Returns: **A `pydantic-core` `CoreSchema`.** .. py:method:: __get_pydantic_json_schema__(core_schema, handler, /) :classmethod: Hook into generating the model's JSON schema. :param core_schema: A `pydantic-core` CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`), or just call the handler with the original schema. :param handler: Call into Pydantic's internal JSON schema generation. This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema generation fails. Since this gets called by `BaseModel.model_json_schema` you can override the `schema_generator` argument to that function to change JSON schema generation globally for a type. :Returns: **A JSON schema, as a Python object.** .. py:method:: __pydantic_init_subclass__(**kwargs) :classmethod: This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass` only after the class is actually fully initialized. In particular, attributes like `model_fields` will be present when this is called. This is necessary because `__init_subclass__` will always be called by `type.__new__`, and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that `type.__new__` was called in such a manner that the class would already be sufficiently initialized. This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely, any kwargs passed to the class definition that aren't used internally by pydantic. :param \*\*kwargs: Any keyword arguments passed to the class definition that aren't used internally by pydantic. .. py:method:: __class_getitem__(typevar_values) :classmethod: .. py:method:: __copy__() Returns a shallow copy of the model. .. py:method:: __deepcopy__(memo = None) Returns a deep copy of the model. .. py:method:: __getattr__(item) .. py:method:: _check_frozen(name, value) .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __eq__(other) .. py:method:: __init_subclass__(**kwargs) :classmethod: This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs. ```py from pydantic import BaseModel class MyModel(BaseModel, extra='allow'): ... ``` However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.) :param \*\*kwargs: Keyword arguments passed to the class definition, which set model_config .. note:: You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called *after* the class is fully initialized. .. py:method:: __iter__() So `dict(model)` works. .. py:method:: __repr__() .. py:method:: __repr_args__() .. py:attribute:: __repr_name__ .. py:attribute:: __repr_str__ .. py:attribute:: __pretty__ .. py:attribute:: __rich_repr__ .. py:method:: __str__() .. py:property:: __fields__ :type: dict[str, pydantic.fields.FieldInfo] .. py:property:: __fields_set__ :type: set[str] .. py:method:: dict(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False) .. py:method:: json(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, encoder = PydanticUndefined, models_as_dict = PydanticUndefined, **dumps_kwargs) .. py:method:: parse_obj(obj) :classmethod: .. py:method:: parse_raw(b, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: parse_file(path, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: from_orm(obj) :classmethod: .. py:method:: construct(_fields_set = None, **values) :classmethod: .. py:method:: copy(*, include = None, exclude = None, update = None, deep = False) Returns a copy of the model. !!! warning "Deprecated" This method is now deprecated; use `model_copy` instead. If you need `include` or `exclude`, use: ```py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) ``` :param include: Optional set or mapping specifying which fields to include in the copied model. :param exclude: Optional set or mapping specifying which fields to exclude in the copied model. :param update: Optional dictionary of field-value pairs to override field values in the copied model. :param deep: If True, the values of fields that are Pydantic models will be deep-copied. :Returns: **A copy of the model with included, excluded and updated fields as specified.** .. py:method:: schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE) :classmethod: .. py:method:: schema_json(*, by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, **dumps_kwargs) :classmethod: .. py:method:: validate(value) :classmethod: .. py:method:: update_forward_refs(**localns) :classmethod: .. py:method:: _iter(*args, **kwargs) .. py:method:: _copy_and_set_values(*args, **kwargs) .. py:method:: _get_value(*args, **kwargs) :classmethod: .. py:method:: _calculate_keys(*args, **kwargs) .. py:function:: serialise(serialiser_type, obj, target_folder, overwrite = False) Serialise an object based on the specified serialiser type. :param serialiser_type: The serialiser type. :type serialiser_type: Union[str, AvailableSerialisers] :param obj: The object to serialise. :type obj: Serialisable :param target_folder: The folder to serialise into. :type target_folder: str :param overwrite: Whether to allow overwriting. Defaults to False. :type overwrite: bool .. py:class:: RelExtrBaseModel(relcat_config, model_config, pretrained_model_name_or_path) Bases: :py:obj:`BaseModelBluePrint` Base class for the RelCAT models .. py:attribute:: name :value: 'basemodel_relcat' .. py:method:: __init__(relcat_config, model_config, pretrained_model_name_or_path) Class to hold the HF model + model_config :param pretrained_model_name_or_path: path to load the model from, this can be a HF model i.e: "bert-base-uncased", if left empty, it is normally assumed that a model is loaded from 'model.dat' using the RelCAT.load() method. So if you are initializing/training a model from scratch be sure to base it on some model. :type pretrained_model_name_or_path: str :param relcat_config: relcat config. :type relcat_config: ConfigRelCAT :param model_config: HF bert config for model. :type model_config: PretrainedConfig .. py:attribute:: relcat_config :type: medcat.config.config_rel_cat.ConfigRelCAT .. py:attribute:: model_config :type: medcat.components.addons.relation_extraction.config.RelExtrBaseConfig .. py:attribute:: hf_model .. py:attribute:: pretrained_model_name_or_path :type: str .. py:method:: _reinitialize_dense_and_frozen_layers(relcat_config) Reinitialize the dense layers of the model :param relcat_config: relcat config. :type relcat_config: ConfigRelCAT .. py:method:: forward(input_ids = None, attention_mask = None, token_type_ids = None, position_ids = None, head_mask = None, encoder_hidden_states = None, encoder_attention_mask = None, Q = None, e1_e2_start = None, pooled_output = None) Forward pass for the model :param input_ids: input token ids. Defaults to None. :type input_ids: torch.Tensor :param attention_mask: attention mask for the input ids. Defaults to None. :type attention_mask: torch.Tensor :param token_type_ids: token type ids for the input ids. Defaults to None. :type token_type_ids: torch.Tensor :param position_ids: The position IDs. Defaults to None. :type position_ids: Any :param head_mask: The head mask. Defaults to None. :type head_mask: Any :param encoder_hidden_states: Encoder hidden states. Defaults to None. :type encoder_hidden_states: Any :param encoder_attention_mask: Encoder attention mask. Defaults to None. :type encoder_attention_mask: Any :param Q: Q. Defaults to None. :type Q: Any :param e1_e2_start: Start and end indices for the entities in the input ids. Defaults to None. :type e1_e2_start: Any :param pooled_output: The pooled output. Defaults to None. :type pooled_output: Any :Returns: **Optional[tuple[torch.Tensor, torch.Tensor]]** -- Logits for the relation classification task. .. py:method:: output2logits(pooled_output, sequence_output, input_ids, e1_e2_start) :param pooled_output: embedding of the CLS token :type pooled_output: torch.Tensor :param sequence_output: hidden states/embeddings for each token in the input text :type sequence_output: torch.Tensor :param input_ids: input token ids. :type input_ids: torch.Tensor :param e1_e2_start: annotation tags token position :type e1_e2_start: torch.Tensor :Returns: **torch.Tensor** -- classification probabilities for each token. .. py:method:: load(pretrained_model_name_or_path, relcat_config, model_config) :classmethod: Load the model from the given path :param pretrained_model_name_or_path: path to load the model from. :type pretrained_model_name_or_path: str :param relcat_config: relcat config. :type relcat_config: ConfigRelCAT :param model_config: The model-specific config. :type model_config: RelExtrBaseConfig :returns: **RelExtrBaseModel** -- The loaded model. .. py:attribute:: drop_out :type: torch.nn.Dropout .. py:attribute:: fc1 :type: torch.nn.Linear .. py:attribute:: fc2 :type: torch.nn.Linear .. py:attribute:: fc3 :type: torch.nn.Linear .. py:attribute:: dump_patches :type: bool :value: False .. py:attribute:: _version :type: int :value: 1 This allows better BC support for :meth:`load_state_dict`. In :meth:`state_dict`, the version number will be saved as in the attribute `_metadata` of the returned state dict, and thus pickled. `_metadata` is a dictionary with keys that follow the naming convention of state dict. See ``_load_from_state_dict`` on how to use this information in loading. If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module's `_load_from_state_dict` method can compare the version number and do appropriate changes if the state dict is from before the change. .. py:attribute:: training :type: bool .. py:attribute:: _parameters :type: Dict[str, Optional[torch.nn.parameter.Parameter]] .. py:attribute:: _buffers :type: Dict[str, Optional[torch.Tensor]] .. py:attribute:: _non_persistent_buffers_set :type: Set[str] .. py:attribute:: _backward_pre_hooks :type: Dict[int, Callable] .. py:attribute:: _backward_hooks :type: Dict[int, Callable] .. py:attribute:: _is_full_backward_hook :type: Optional[bool] .. py:attribute:: _forward_hooks :type: Dict[int, Callable] .. py:attribute:: _forward_hooks_with_kwargs :type: Dict[int, bool] .. py:attribute:: _forward_hooks_always_called :type: Dict[int, bool] .. py:attribute:: _forward_pre_hooks :type: Dict[int, Callable] .. py:attribute:: _forward_pre_hooks_with_kwargs :type: Dict[int, bool] .. py:attribute:: _state_dict_hooks :type: Dict[int, Callable] .. py:attribute:: _load_state_dict_pre_hooks :type: Dict[int, Callable] .. py:attribute:: _state_dict_pre_hooks :type: Dict[int, Callable] .. py:attribute:: _load_state_dict_post_hooks :type: Dict[int, Callable] .. py:attribute:: _modules :type: Dict[str, Optional[Module]] .. py:attribute:: call_super_init :type: bool :value: False .. py:attribute:: _compiled_call_impl :type: Optional[Callable] :value: None .. py:method:: register_buffer(name, tensor, persistent = True) Add a buffer to the module. This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm's ``running_mean`` is not a parameter, but is part of the module's state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting :attr:`persistent` to ``False``. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module's :attr:`state_dict`. Buffers can be accessed as attributes using given names. :param name: name of the buffer. The buffer can be accessed from this module using the given name :type name: str :param tensor: buffer to be registered. If ``None``, then operations that run on buffers, such as :attr:`cuda`, are ignored. If ``None``, the buffer is **not** included in the module's :attr:`state_dict`. :type tensor: Tensor or None :param persistent: whether the buffer is part of this module's :attr:`state_dict`. :type persistent: bool Example:: >>> # xdoctest: +SKIP("undefined vars") >>> self.register_buffer('running_mean', torch.zeros(num_features)) .. py:method:: register_parameter(name, param) Add a parameter to the module. The parameter can be accessed as an attribute using given name. :param name: name of the parameter. The parameter can be accessed from this module using the given name :type name: str :param param: parameter to be added to the module. If ``None``, then operations that run on parameters, such as :attr:`cuda`, are ignored. If ``None``, the parameter is **not** included in the module's :attr:`state_dict`. :type param: Parameter or None .. py:method:: add_module(name, module) Add a child module to the current module. The module can be accessed as an attribute using the given name. :param name: name of the child module. The child module can be accessed from this module using the given name :type name: str :param module: child module to be added to the module. :type module: Module .. py:method:: register_module(name, module) Alias for :func:`add_module`. .. py:method:: get_submodule(target) Return the submodule given by ``target`` if it exists, otherwise throw an error. For example, let's say you have an ``nn.Module`` ``A`` that looks like this: .. code-block:: text A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) ) (The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested submodule ``net_b``, which itself has two submodules ``net_c`` and ``linear``. ``net_c`` then has a submodule ``conv``.) To check whether or not we have the ``linear`` submodule, we would call ``get_submodule("net_b.linear")``. To check whether we have the ``conv`` submodule, we would call ``get_submodule("net_b.net_c.conv")``. The runtime of ``get_submodule`` is bounded by the degree of module nesting in ``target``. A query against ``named_modules`` achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, ``get_submodule`` should always be used. :param target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) :Returns: **torch.nn.Module** -- The submodule referenced by ``target`` :raises AttributeError: If the target string references an invalid path or resolves to something that is not an ``nn.Module`` .. py:method:: set_submodule(target, module) Set the submodule given by ``target`` if it exists, otherwise throw an error. For example, let's say you have an ``nn.Module`` ``A`` that looks like this: .. code-block:: text A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) ) (The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested submodule ``net_b``, which itself has two submodules ``net_c`` and ``linear``. ``net_c`` then has a submodule ``conv``.) To overide the ``Conv2d`` with a new submodule ``Linear``, you would call ``set_submodule("net_b.net_c.conv", nn.Linear(33, 16))``. :param target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) :param module: The module to set the submodule to. :raises ValueError: If the target string is empty :raises AttributeError: If the target string references an invalid path or resolves to something that is not an ``nn.Module`` .. py:method:: get_parameter(target) Return the parameter given by ``target`` if it exists, otherwise throw an error. See the docstring for ``get_submodule`` for a more detailed explanation of this method's functionality as well as how to correctly specify ``target``. :param target: The fully-qualified string name of the Parameter to look for. (See ``get_submodule`` for how to specify a fully-qualified string.) :Returns: **torch.nn.Parameter** -- The Parameter referenced by ``target`` :raises AttributeError: If the target string references an invalid path or resolves to something that is not an ``nn.Parameter`` .. py:method:: get_buffer(target) Return the buffer given by ``target`` if it exists, otherwise throw an error. See the docstring for ``get_submodule`` for a more detailed explanation of this method's functionality as well as how to correctly specify ``target``. :param target: The fully-qualified string name of the buffer to look for. (See ``get_submodule`` for how to specify a fully-qualified string.) :Returns: **torch.Tensor** -- The buffer referenced by ``target`` :raises AttributeError: If the target string references an invalid path or resolves to something that is not a buffer .. py:method:: get_extra_state() Return any extra state to include in the module's state_dict. Implement this and a corresponding :func:`set_extra_state` for your module if you need to store extra state. This function is called when building the module's `state_dict()`. Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes. :Returns: **object** -- Any extra state to store in the module's state_dict .. py:method:: set_extra_state(state) Set extra state contained in the loaded `state_dict`. This function is called from :func:`load_state_dict` to handle any extra state found within the `state_dict`. Implement this function and a corresponding :func:`get_extra_state` for your module if you need to store extra state within its `state_dict`. :param state: Extra state from the `state_dict` :type state: dict .. py:method:: _apply(fn, recurse=True) .. py:method:: apply(fn) Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self. Typical use includes initializing the parameters of a model (see also :ref:`nn-init-doc`). :param fn: function to be applied to each submodule :type fn: :class:`Module` -> None :Returns: **Module** -- self Example:: >>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) .. py:method:: cuda(device = None) Move all model parameters and buffers to the GPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :Returns: **Module** -- self .. py:method:: ipu(device = None) Move all model parameters and buffers to the IPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :Returns: **Module** -- self .. py:method:: xpu(device = None) Move all model parameters and buffers to the XPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :Returns: **Module** -- self .. py:method:: mtia(device = None) Move all model parameters and buffers to the MTIA. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized. .. note:: This method modifies the module in-place. :param device: if specified, all parameters will be copied to that device :type device: int, optional :Returns: **Module** -- self .. py:method:: cpu() Move all model parameters and buffers to the CPU. .. note:: This method modifies the module in-place. :Returns: **Module** -- self .. py:method:: type(dst_type) Casts all parameters and buffers to :attr:`dst_type`. .. note:: This method modifies the module in-place. :param dst_type: the desired type :type dst_type: type or string :Returns: **Module** -- self .. py:method:: float() Casts all floating point parameters and buffers to ``float`` datatype. .. note:: This method modifies the module in-place. :Returns: **Module** -- self .. py:method:: double() Casts all floating point parameters and buffers to ``double`` datatype. .. note:: This method modifies the module in-place. :Returns: **Module** -- self .. py:method:: half() Casts all floating point parameters and buffers to ``half`` datatype. .. note:: This method modifies the module in-place. :Returns: **Module** -- self .. py:method:: bfloat16() Casts all floating point parameters and buffers to ``bfloat16`` datatype. .. note:: This method modifies the module in-place. :Returns: **Module** -- self .. py:method:: to_empty(*, device, recurse = True) Move the parameters and buffers to the specified device without copying storage. :param device: The desired device of the parameters and buffers in this module. :type device: :class:`torch.device` :param recurse: Whether parameters and buffers of submodules should be recursively moved to the specified device. :type recurse: bool :Returns: **Module** -- self .. py:method:: to(device: Optional[torch._prims_common.DeviceLikeType] = ..., dtype: Optional[Module.to.dtype] = ..., non_blocking: bool = ...) -> typing_extensions.Self to(dtype: Module.to.dtype, non_blocking: bool = ...) -> typing_extensions.Self to(tensor: torch.Tensor, non_blocking: bool = ...) -> typing_extensions.Self Move and/or cast the parameters and buffers. This can be called as .. function:: to(device=None, dtype=None, non_blocking=False) :noindex: .. function:: to(dtype, non_blocking=False) :noindex: .. function:: to(tensor, non_blocking=False) :noindex: .. function:: to(memory_format=torch.channels_last) :noindex: Its signature is similar to :meth:`torch.Tensor.to`, but only accepts floating point or complex :attr:`dtype`\ s. In addition, this method will only cast the floating point or complex parameters and buffers to :attr:`dtype` (if given). The integral parameters and buffers will be moved :attr:`device`, if that is given, but with dtypes unchanged. When :attr:`non_blocking` is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices. See below for examples. .. note:: This method modifies the module in-place. :param device: the desired device of the parameters and buffers in this module :type device: :class:`torch.device` :param dtype: the desired floating point or complex dtype of the parameters and buffers in this module :type dtype: :class:`torch.dtype` :param tensor: Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module :type tensor: torch.Tensor :param memory_format: the desired memory format for 4D parameters and buffers in this module (keyword only argument) :type memory_format: :class:`torch.memory_format` :Returns: **Module** -- self Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> linear = nn.Linear(2, 2) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]]) >>> linear.to(torch.double) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]], dtype=torch.float64) >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1) >>> gpu1 = torch.device("cuda:1") >>> linear.to(gpu1, dtype=torch.half, non_blocking=True) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1') >>> cpu = torch.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16) >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble) >>> linear.weight Parameter containing: tensor([[ 0.3741+0.j, 0.2382+0.j], [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128) >>> linear(torch.ones(3, 2, dtype=torch.cdouble)) tensor([[0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128) .. py:method:: register_full_backward_pre_hook(hook, prepend = False) Register a backward pre-hook on the module. The hook will be called every time the gradients for the module are computed. The hook should have the following signature:: hook(module, grad_output) -> tuple[Tensor] or None The :attr:`grad_output` is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place of :attr:`grad_output` in subsequent computations. Entries in :attr:`grad_output` will be ``None`` for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error. :param hook: The user-defined hook to be registered. :type hook: Callable :param prepend: If true, the provided ``hook`` will be fired before all existing ``backward_pre`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``backward_pre`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``backward_pre`` hooks registered with :func:`register_module_full_backward_pre_hook` will fire before all hooks registered by this method. :type prepend: bool :Returns: **:class:`torch.utils.hooks.RemovableHandle`** -- a handle that can be used to remove the added hook by calling ``handle.remove()`` .. py:method:: register_backward_hook(hook) Register a backward hook on the module. This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and the behavior of this function will change in future versions. :Returns: **:class:`torch.utils.hooks.RemovableHandle`** -- a handle that can be used to remove the added hook by calling ``handle.remove()`` .. py:method:: register_full_backward_hook(hook, prepend = False) Register a backward hook on the module. The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> tuple(Tensor) or None The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of :attr:`grad_input` in subsequent computations. :attr:`grad_input` will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error. :param hook: The user-defined hook to be registered. :type hook: Callable :param prepend: If true, the provided ``hook`` will be fired before all existing ``backward`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``backward`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``backward`` hooks registered with :func:`register_module_full_backward_hook` will fire before all hooks registered by this method. :type prepend: bool :Returns: **:class:`torch.utils.hooks.RemovableHandle`** -- a handle that can be used to remove the added hook by calling ``handle.remove()`` .. py:method:: _get_backward_hooks() Return the backward hooks for use in the call function. It returns two lists, one with the full backward hooks and one with the non-full backward hooks. .. py:method:: _get_backward_pre_hooks() .. py:method:: _maybe_warn_non_full_backward_hook(inputs, result, grad_fn) .. py:method:: register_forward_pre_hook(hook, *, prepend = False, with_kwargs = False) Register a forward pre-hook on the module. The hook will be called every time before :func:`forward` is invoked. If ``with_kwargs`` is false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature:: hook(module, args) -> None or modified input If ``with_kwargs`` is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:: hook(module, args, kwargs) -> None or a tuple of modified input and kwargs :param hook: The user defined hook to be registered. :type hook: Callable :param prepend: If true, the provided ``hook`` will be fired before all existing ``forward_pre`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``forward_pre`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``forward_pre`` hooks registered with :func:`register_module_forward_pre_hook` will fire before all hooks registered by this method. Default: ``False`` :type prepend: bool :param with_kwargs: If true, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` :type with_kwargs: bool :Returns: **:class:`torch.utils.hooks.RemovableHandle`** -- a handle that can be used to remove the added hook by calling ``handle.remove()`` .. py:method:: register_forward_hook(hook, *, prepend = False, with_kwargs = False, always_call = False) Register a forward hook on the module. The hook will be called every time after :func:`forward` has computed an output. If ``with_kwargs`` is ``False`` or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:`forward` is called. The hook should have the following signature:: hook(module, args, output) -> None or modified output If ``with_kwargs`` is ``True``, the forward hook will be passed the ``kwargs`` given to the forward function and be expected to return the output possibly modified. The hook should have the following signature:: hook(module, args, kwargs, output) -> None or modified output :param hook: The user defined hook to be registered. :type hook: Callable :param prepend: If ``True``, the provided ``hook`` will be fired before all existing ``forward`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``forward`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``forward`` hooks registered with :func:`register_module_forward_hook` will fire before all hooks registered by this method. Default: ``False`` :type prepend: bool :param with_kwargs: If ``True``, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` :type with_kwargs: bool :param always_call: If ``True`` the ``hook`` will be run regardless of whether an exception is raised while calling the Module. Default: ``False`` :type always_call: bool :Returns: **:class:`torch.utils.hooks.RemovableHandle`** -- a handle that can be used to remove the added hook by calling ``handle.remove()`` .. py:method:: _slow_forward(*input, **kwargs) .. py:method:: _wrapped_call_impl(*args, **kwargs) .. py:method:: _call_impl(*args, **kwargs) .. py:attribute:: __call__ :type: Callable[Ellipsis, Any] .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __getattr__(name) .. py:method:: __setattr__(name, value) .. py:method:: __delattr__(name) .. py:method:: _register_state_dict_hook(hook) Register a post-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, state_dict, prefix, local_metadata) -> None or state_dict The registered hooks can modify the ``state_dict`` inplace or return a new one. If a new ``state_dict`` is returned, it will only be respected if it is the root module that :meth:`~nn.Module.state_dict` is called from. .. py:method:: register_state_dict_post_hook(hook) Register a post-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, state_dict, prefix, local_metadata) -> None The registered hooks can modify the ``state_dict`` inplace. .. py:method:: register_state_dict_pre_hook(hook) Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, prefix, keep_vars) -> None The registered hooks can be used to perform pre-processing before the ``state_dict`` call is made. .. py:method:: _save_to_state_dict(destination, prefix, keep_vars) Save module state to the `destination` dictionary. The `destination` dictionary will contain the state of the module, but not its descendants. This is called on every submodule in :meth:`~torch.nn.Module.state_dict`. In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic. :param destination: a dict where state will be stored :type destination: dict :param prefix: the prefix for parameters and buffers used in this module :type prefix: str .. py:attribute:: T_destination .. py:method:: state_dict(*, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination state_dict(*, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Any] Return a dictionary containing references to the whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to ``None`` are not included. .. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers. .. warning:: Currently ``state_dict()`` also accepts positional arguments for ``destination``, ``prefix`` and ``keep_vars`` in order. However, this is being deprecated and keyword arguments will be enforced in future releases. .. warning:: Please avoid the use of argument ``destination`` as it is not designed for end-users. :param destination: If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an ``OrderedDict`` will be created and returned. Default: ``None``. :type destination: dict, optional :param prefix: a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ``''``. :type prefix: str, optional :param keep_vars: by default the :class:`~torch.Tensor` s returned in the state dict are detached from autograd. If it's set to ``True``, detaching will not be performed. Default: ``False``. :type keep_vars: bool, optional :Returns: **dict** -- a dictionary containing a whole state of the module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight'] .. py:method:: _register_load_state_dict_pre_hook(hook, with_module=False) See :meth:`~torch.nn.Module.register_load_state_dict_pre_hook` for details. A subtle difference is that if ``with_module`` is set to ``False``, then the hook will not take the ``module`` as the first argument whereas :meth:`~torch.nn.Module.register_load_state_dict_pre_hook` always takes the ``module`` as the first argument. :param hook: Callable hook that will be invoked before loading the state dict. :type hook: Callable :param with_module: Whether or not to pass the module instance to the hook as the first parameter. :type with_module: bool, optional .. py:method:: register_load_state_dict_pre_hook(hook) Register a pre-hook to be run before module's :meth:`~nn.Module.load_state_dict` is called. It should have the following signature:: hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950 :param hook: Callable hook that will be invoked before loading the state dict. :type hook: Callable .. py:method:: register_load_state_dict_post_hook(hook) Register a post-hook to be run after module's :meth:`~nn.Module.load_state_dict` is called. It should have the following signature:: hook(module, incompatible_keys) -> None The ``module`` argument is the current module that this hook is registered on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys`` is a ``list`` of ``str`` containing the missing keys and ``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys. The given incompatible_keys can be modified inplace if needed. Note that the checks performed when calling :func:`load_state_dict` with ``strict=True`` are affected by modifications the hook makes to ``missing_keys`` or ``unexpected_keys``, as expected. Additions to either set of keys will result in an error being thrown when ``strict=True``, and clearing out both missing and unexpected keys will avoid an error. :Returns: **:class:`torch.utils.hooks.RemovableHandle`** -- a handle that can be used to remove the added hook by calling ``handle.remove()`` .. py:method:: _load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) Copy parameters and buffers from :attr:`state_dict` into only this module, but not its descendants. This is called on every submodule in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this module in input :attr:`state_dict` is provided as :attr:`local_metadata`. For state dicts without metadata, :attr:`local_metadata` is empty. Subclasses can achieve class-specific backward compatible loading using the version number at `local_metadata.get("version", None)`. Additionally, :attr:`local_metadata` can also contain the key `assign_to_params_buffers` that indicates whether keys should be assigned their corresponding tensor in the state_dict. .. note:: :attr:`state_dict` is not the same object as the input :attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So it can be modified. :param state_dict: a dict containing parameters and persistent buffers. :type state_dict: dict :param prefix: the prefix for parameters and buffers used in this module :type prefix: str :param local_metadata: a dict containing the metadata for this module. See :type local_metadata: dict :param strict: whether to strictly enforce that the keys in :attr:`state_dict` with :attr:`prefix` match the names of parameters and buffers in this module :type strict: bool :param missing_keys: if ``strict=True``, add missing keys to this list :type missing_keys: list of str :param unexpected_keys: if ``strict=True``, add unexpected keys to this list :type unexpected_keys: list of str :param error_msgs: error messages should be added to this list, and will be reported together in :meth:`~torch.nn.Module.load_state_dict` :type error_msgs: list of str .. py:method:: load_state_dict(state_dict, strict = True, assign = False) Copy parameters and buffers from :attr:`state_dict` into this module and its descendants. If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. .. warning:: If :attr:`assign` is ``True`` the optimizer must be created after the call to :attr:`load_state_dict` unless :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``. :param state_dict: a dict containing parameters and persistent buffers. :type state_dict: dict :param strict: whether to strictly enforce that the keys in :attr:`state_dict` match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. Default: ``True`` :type strict: bool, optional :param assign: When ``False``, the properties of the tensors in the current module are preserved while when ``True``, the properties of the Tensors in the state dict are preserved. The only exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s for which the value from the module is preserved. Default: ``False`` :type assign: bool, optional :Returns: **``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields** -- * **missing_keys** is a list of str containing any keys that are expected by this module but missing from the provided ``state_dict``. * **unexpected_keys** is a list of str containing the keys that are not expected by this module but present in the provided ``state_dict``. .. note:: If a parameter or buffer is registered as ``None`` and its corresponding key exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a ``RuntimeError``. .. py:method:: _named_members(get_members_fn, prefix='', recurse=True, remove_duplicate = True) Help yield various names + members of modules. .. py:method:: parameters(recurse = True) Return an iterator over module parameters. This is typically passed to an optimizer. :param recurse: if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. :type recurse: bool :Yields: *Parameter* -- module parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for param in model.parameters(): >>> print(type(param), param.size()) (20L,) (20L, 1L, 5L, 5L) .. py:method:: named_parameters(prefix = '', recurse = True, remove_duplicate = True) Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. :param prefix: prefix to prepend to all parameter names. :type prefix: str :param recurse: if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. :type recurse: bool :param remove_duplicate: whether to remove the duplicated parameters in the result. Defaults to True. :type remove_duplicate: bool, optional :Yields: *(str, Parameter)* -- Tuple containing the name and parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size()) .. py:method:: buffers(recurse = True) Return an iterator over module buffers. :param recurse: if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. :type recurse: bool :Yields: *torch.Tensor* -- module buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) (20L,) (20L, 1L, 5L, 5L) .. py:method:: named_buffers(prefix = '', recurse = True, remove_duplicate = True) Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. :param prefix: prefix to prepend to all buffer names. :type prefix: str :param recurse: if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. :type recurse: bool, optional :param remove_duplicate: whether to remove the duplicated buffers in the result. Defaults to True. :type remove_duplicate: bool, optional :Yields: *(str, torch.Tensor)* -- Tuple containing the name and buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size()) .. py:method:: children() Return an iterator over immediate children modules. :Yields: *Module* -- a child module .. py:method:: named_children() Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. :Yields: *(str, Module)* -- Tuple containing a name and child module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module) .. py:method:: modules() Return an iterator over all modules in the network. :Yields: *Module* -- a module in the network .. note:: Duplicate modules are returned only once. In the following example, ``l`` will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): ... print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True) .. py:method:: named_modules(memo = None, prefix = '', remove_duplicate = True) Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. :param memo: a memo to store the set of modules already added to the result :param prefix: a prefix that will be added to the name of the module :param remove_duplicate: whether to remove the duplicated module instances in the result or not :Yields: *(str, Module)* -- Tuple of name and module .. note:: Duplicate modules are returned only once. In the following example, ``l`` will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): ... print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True)) .. py:method:: train(mode = True) Set the module in training mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. :param mode: whether to set training mode (``True``) or evaluation mode (``False``). Default: ``True``. :type mode: bool :Returns: **Module** -- self .. py:method:: eval() Set the module in evaluation mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. This is equivalent with :meth:`self.train(False) `. See :ref:`locally-disable-grad-doc` for a comparison between `.eval()` and several similar mechanisms that may be confused with it. :Returns: **Module** -- self .. py:method:: requires_grad_(requires_grad = True) Change if autograd should record operations on parameters in this module. This method sets the parameters' :attr:`requires_grad` attributes in-place. This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training). See :ref:`locally-disable-grad-doc` for a comparison between `.requires_grad_()` and several similar mechanisms that may be confused with it. :param requires_grad: whether autograd should record operations on parameters in this module. Default: ``True``. :type requires_grad: bool :Returns: **Module** -- self .. py:method:: zero_grad(set_to_none = True) Reset gradients of all model parameters. See similar function under :class:`torch.optim.Optimizer` for more context. :param set_to_none: instead of setting to zero, set the grads to None. See :meth:`torch.optim.Optimizer.zero_grad` for details. :type set_to_none: bool .. py:method:: share_memory() See :meth:`torch.Tensor.share_memory_`. .. py:method:: _get_name() .. py:method:: extra_repr() Set the extra representation of the module. To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable. .. py:method:: __repr__() .. py:method:: __dir__() .. py:method:: _replicate_for_data_parallel() .. py:method:: compile(*args, **kwargs) Compile this Module's forward using :func:`torch.compile`. This Module's `__call__` method is compiled and all arguments are passed as-is to :func:`torch.compile`. See :func:`torch.compile` for details on the arguments for this function. .. py:class:: Pad_Sequence(seq_pad_value, label_pad_value = -1) .. py:method:: __init__(seq_pad_value, label_pad_value = -1) Used in rel_cat.py in RelCAT to create DataLoaders for train/test datasets. collate_fn for dataloader to collate sequences of different input_ids, ent1/ent2, and label lengths into a fixed length batch. This is applied per batch and not on the whole DataLoader data, padded x sequence, y sequence, x lengths and y lengths of batch. :param seq_pad_value: pad value for input_ids. :type seq_pad_value: int :param label_pad_value: pad value for labels. Defaults to -1. :type label_pad_value: int .. py:attribute:: seq_pad_value :type: int .. py:attribute:: label_pad_value :type: int :value: -1 .. py:method:: __call__(batch) Pads a batch of input_ids. :param batch: gets the batch of Tensors from RelData.dataset (check __getitem__() method for data returned) and pads the token sequence + labels as needed See https://pytorch.org/docs/stable/_modules/torch/nn/utils/rnn.html#pad_sequence for extra info. :type batch: list[torch.Tensor] :Returns: **tuple[Tensor, Tensor, Tensor, LongTensor, LongTensor]** -- padded data padded input ids, ent1&ent2 start token pos, padded labels, padded input_id_lengths, padded labels length .. py:class:: BaseTokenizerWrapper(hf_tokenizers=None, max_seq_length = None, add_special_tokens = False) Bases: :py:obj:`transformers.PreTrainedTokenizerFast` Base class for all fast tokenizers (wrapping HuggingFace tokenizers library). Inherits from [`~tokenization_utils_base.PreTrainedTokenizerBase`]. Handles all the shared methods for tokenization and special tokens, as well as methods for downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary. This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...). .. py:attribute:: name :value: 'base_tokenizer_wrapper_rel' .. py:method:: __init__(hf_tokenizers=None, max_seq_length = None, add_special_tokens = False) .. py:attribute:: hf_tokenizers :value: None .. py:attribute:: max_seq_length :value: None .. py:attribute:: _add_special_tokens :value: False .. py:method:: get_size() .. py:method:: token_to_id(token) .. py:method:: get_pad_id() .. py:method:: __call__(text, truncation = True) Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences. :param text: The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). :type text: `str`, `List[str]`, `List[List[str]]`, *optional* :param text_pair: The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). :type text_pair: `str`, `List[str]`, `List[List[str]]`, *optional* :param text_target: The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). :type text_target: `str`, `List[str]`, `List[List[str]]`, *optional* :param text_pair_target: The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). :type text_pair_target: `str`, `List[str]`, `List[List[str]]`, *optional* .. py:method:: save(dir_path) .. py:method:: load(tokenizer_path, relcat_config, **kwargs) :classmethod: .. py:attribute:: _backends :value: ['tokenizers'] .. py:attribute:: vocab_files_names .. py:attribute:: slow_tokenizer_class :type: transformers.tokenization_utils.PreTrainedTokenizer :value: None .. py:attribute:: add_prefix_space .. py:attribute:: _tokenizer .. py:attribute:: _decode_use_source_tokenizer :value: False .. py:property:: is_fast :type: bool .. py:property:: can_save_slow_tokenizer :type: bool Whether or not the slow tokenizer can be saved. Usually for sentencepiece based slow tokenizer, this can only be `True` if the original `"sentencepiece.model"` was not deleted. :type: `bool` .. py:property:: vocab_size :type: int Size of the base vocabulary (without the added tokens). :type: `int` .. py:method:: get_vocab() Returns the vocabulary as a dictionary of token to index. `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the vocab. :Returns: **`Dict[str, int]`** -- The vocabulary. .. py:property:: vocab :type: Dict[str, int] .. py:property:: added_tokens_encoder :type: Dict[str, int] Returns the sorted mapping from string to index. The added tokens encoder is cached for performance optimisation in `self._added_tokens_encoder` for the slow tokenizers. .. py:property:: added_tokens_decoder :type: Dict[int, transformers.tokenization_utils_base.AddedToken] Returns the added tokens in the vocabulary as a dictionary of index to AddedToken. :Returns: **`Dict[str, int]`** -- The added tokens. .. py:method:: get_added_vocab() Returns the added tokens in the vocabulary as a dictionary of token to index. :Returns: **`Dict[str, int]`** -- The added tokens. .. py:method:: __len__() Size of the full vocabulary with the added tokens. .. py:property:: backend_tokenizer :type: tokenizers.Tokenizer The Rust tokenizer used as a backend. :type: `tokenizers.implementations.BaseTokenizer` .. py:property:: decoder :type: tokenizers.decoders.Decoder The Rust decoder for this tokenizer. :type: `tokenizers.decoders.Decoder` .. py:method:: _convert_encoding(encoding, return_token_type_ids = None, return_attention_mask = None, return_overflowing_tokens = False, return_special_tokens_mask = False, return_offsets_mapping = False, return_length = False, verbose = True) Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list of encodings, take care of building a batch from overflowing tokens. Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are lists (overflows) of lists (tokens). Output shape: (overflows, sequence length) .. py:method:: convert_tokens_to_ids(tokens) Converts a token string (or a sequence of tokens) in a single integer id (or a Iterable of ids), using the vocabulary. :param tokens: One or several token(s) to convert to token id(s). :type tokens: `str` or `Iterable[str]` :Returns: **`int` or `List[int]`** -- The token id or list of token ids. .. py:method:: _convert_token_to_id_with_added_voc(token) .. py:method:: _convert_id_to_token(index) .. py:method:: _add_tokens(new_tokens, special_tokens=False) .. py:method:: num_special_tokens_to_add(pair = False) Returns the number of added tokens when encoding a sequence with special tokens. This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop. :param pair: Whether the number of added tokens should be computed in the case of a sequence pair or a single sequence. :type pair: `bool`, *optional*, defaults to `False` :Returns: **`int`** -- Number of special tokens added to sequences. .. py:method:: convert_ids_to_tokens(ids, skip_special_tokens = False) Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and added tokens. :param ids: The token id (or token ids) to convert to tokens. :type ids: `int` or `List[int]` :param skip_special_tokens: Whether or not to remove special tokens in the decoding. :type skip_special_tokens: `bool`, *optional*, defaults to `False` :Returns: **`str` or `List[str]`** -- The decoded token(s). .. py:method:: tokenize(text, pair = None, add_special_tokens = False, **kwargs) Converts a string into a sequence of tokens, replacing unknown tokens with the `unk_token`. :param text: The sequence to be encoded. :type text: `str` :param pair: A second sequence to be encoded with the first. :type pair: `str`, *optional* :param add_special_tokens: Whether or not to add the special tokens associated with the corresponding model. :type add_special_tokens: `bool`, *optional*, defaults to `False` :param kwargs: Will be passed to the underlying model specific encode method. See details in [`~PreTrainedTokenizerBase.__call__`] :type kwargs: additional keyword arguments, *optional* :Returns: **`List[str]`** -- The list of tokens. .. py:method:: set_truncation_and_padding(padding_strategy, truncation_strategy, max_length, stride, pad_to_multiple_of, padding_side) Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers library) and restore the tokenizer settings afterwards. The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed section. :param padding_strategy: The kind of padding that will be applied to the input :type padding_strategy: [`~utils.PaddingStrategy`] :param truncation_strategy: The kind of truncation that will be applied to the input :type truncation_strategy: [`~tokenization_utils_base.TruncationStrategy`] :param max_length: The maximum size of a sequence. :type max_length: `int` :param stride: The stride to use when handling overflow. :type stride: `int` :param pad_to_multiple_of: If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). :type pad_to_multiple_of: `int`, *optional* :param padding_side: The side on which the model should have padding applied. Should be selected between ['right', 'left']. Default value is picked from the class attribute of the same name. :type padding_side: `str`, *optional* .. py:method:: _batch_encode_plus(batch_text_or_text_pairs, add_special_tokens = True, padding_strategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length = None, stride = 0, is_split_into_words = False, pad_to_multiple_of = None, padding_side = None, return_tensors = None, return_token_type_ids = None, return_attention_mask = None, return_overflowing_tokens = False, return_special_tokens_mask = False, return_offsets_mapping = False, return_length = False, verbose = True, split_special_tokens = False) .. py:method:: _encode_plus(text, text_pair = None, add_special_tokens = True, padding_strategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length = None, stride = 0, is_split_into_words = False, pad_to_multiple_of = None, padding_side = None, return_tensors = None, return_token_type_ids = None, return_attention_mask = None, return_overflowing_tokens = False, return_special_tokens_mask = False, return_offsets_mapping = False, return_length = False, verbose = True, split_special_tokens = False, **kwargs) .. py:method:: convert_tokens_to_string(tokens) Converts a sequence of tokens in a single string. The most simple way to do it is `" ".join(tokens)` but we often want to remove sub-word tokenization artifacts at the same time. :param tokens: The token to join in a string. :type tokens: `List[str]` :Returns: **`str`** -- The joined tokens. .. py:method:: _decode(token_ids, skip_special_tokens = False, clean_up_tokenization_spaces = None, **kwargs) .. py:method:: _save_pretrained(save_directory, file_names, legacy_format = None, filename_prefix = None) Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens as well as in a unique JSON file containing {config + vocab + added-tokens}. .. py:method:: train_new_from_iterator(text_iterator, vocab_size, length=None, new_special_tokens=None, special_tokens_map=None, **kwargs) Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline) as the current one. :param text_iterator: The training corpus. Should be a generator of batches of texts, for instance a list of lists of texts if you have everything in memory. :type text_iterator: generator of `List[str]` :param vocab_size: The size of the vocabulary you want for your tokenizer. :type vocab_size: `int` :param length: The total number of sequences in the iterator. This is used to provide meaningful progress tracking :type length: `int`, *optional* :param new_special_tokens: A list of new special tokens to add to the tokenizer you are training. :type new_special_tokens: list of `str` or `AddedToken`, *optional* :param special_tokens_map: If you want to rename some of the special tokens this tokenizer uses, pass along a mapping old special token name to new special token name in this argument. :type special_tokens_map: `Dict[str, str]`, *optional* :param kwargs: Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library. :type kwargs: `Dict[str, Any]`, *optional* :Returns: * **[`PreTrainedTokenizerFast`]** -- A new tokenizer of the same type as the original one, trained on * **`text_iterator`.** .. py:attribute:: pretrained_vocab_files_map :type: Dict[str, Dict[str, str]] .. py:attribute:: _auto_class :type: Optional[str] :value: None .. py:attribute:: model_input_names :type: List[str] :value: ['input_ids', 'token_type_ids', 'attention_mask'] .. py:attribute:: padding_side :type: str :value: 'right' .. py:attribute:: truncation_side :type: str :value: 'right' .. py:attribute:: init_inputs :value: () .. py:attribute:: init_kwargs .. py:attribute:: name_or_path .. py:attribute:: _processor_class .. py:attribute:: model_max_length .. py:attribute:: clean_up_tokenization_spaces .. py:attribute:: split_special_tokens .. py:attribute:: deprecation_warnings .. py:attribute:: _in_target_context_manager :value: False .. py:attribute:: chat_template .. py:attribute:: extra_special_tokens .. py:property:: max_len_single_sentence :type: int The maximum length of a sentence that can be fed to the model. :type: `int` .. py:property:: max_len_sentences_pair :type: int The maximum combined length of a pair of sentences that can be fed to the model. :type: `int` .. py:method:: _set_processor_class(processor_class) Sets processor class as an attribute. .. py:method:: __repr__() .. py:method:: apply_chat_template(conversation, tools = None, documents = None, chat_template = None, add_generation_prompt = False, continue_final_message = False, tokenize = True, padding = False, truncation = False, max_length = None, return_tensors = None, return_dict = False, return_assistant_tokens_mask = False, tokenizer_kwargs = None, **kwargs) Converts a list of dictionaries with `"role"` and `"content"` keys to a list of token ids. This method is intended for use with chat models, and will read the tokenizer's chat_template attribute to determine the format and control tokens to use when converting. :param conversation: A list of dicts with "role" and "content" keys, representing the chat history so far. :type conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]] :param tools: A list of tools (callable functions) that will be accessible to the model. If the template does not support function calling, this argument will have no effect. Each tool should be passed as a JSON Schema, giving the name, description and argument types for the tool. See our [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#automated-function-conversion-for-tool-use) for more information. :type tools: `List[Dict]`, *optional* :param documents: A list of dicts representing documents that will be accessible to the model if it is performing RAG (retrieval-augmented generation). If the template does not support RAG, this argument will have no effect. We recommend that each document should be a dict containing "title" and "text" keys. Please see the RAG section of the [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#arguments-for-RAG) for examples of passing documents with chat templates. :type documents: `List[Dict[str, str]]`, *optional* :param chat_template: A Jinja template to use for this conversion. It is usually not necessary to pass anything to this argument, as the model's template will be used by default. :type chat_template: `str`, *optional* :param add_generation_prompt: If this is set, a prompt with the token(s) that indicate the start of an assistant message will be appended to the formatted output. This is useful when you want to generate a response from the model. Note that this argument will be passed to the chat template, and so it must be supported in the template for this argument to have any effect. :type add_generation_prompt: bool, *optional* :param continue_final_message: If this is set, the chat will be formatted so that the final message in the chat is open-ended, without any EOS tokens. The model will continue this message rather than starting a new one. This allows you to "prefill" part of the model's response for it. Cannot be used at the same time as `add_generation_prompt`. :type continue_final_message: bool, *optional* :param tokenize: Whether to tokenize the output. If `False`, the output will be a string. :type tokenize: `bool`, defaults to `True` :param padding: Whether to pad sequences to the maximum length. Has no effect if tokenize is `False`. :type padding: `bool`, defaults to `False` :param truncation: Whether to truncate sequences at the maximum length. Has no effect if tokenize is `False`. :type truncation: `bool`, defaults to `False` :param max_length: Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is `False`. If not specified, the tokenizer's `max_length` attribute will be used as a default. :type max_length: `int`, *optional* :param return_tensors: If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable values are: - `'tf'`: Return TensorFlow `tf.Tensor` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. :type return_tensors: `str` or [`~utils.TensorType`], *optional* :param return_dict: Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`. :type return_dict: `bool`, defaults to `False` :param tokenizer_kwargs (`Dict[str: Any]`, *optional*): Additional kwargs to pass to the tokenizer. :param return_assistant_tokens_mask: Whether to return a mask of the assistant generated tokens. For tokens generated by the assistant, the mask will contain 1. For user and system tokens, the mask will contain 0. This functionality is only available for chat templates that support it via the `{% generation %}` keyword. :type return_assistant_tokens_mask: `bool`, defaults to `False` :param \*\*kwargs: Additional kwargs to pass to the template renderer. Will be accessible by the chat template. :Returns: * **`Union[List[int], Dict]`** -- A list of token ids representing the tokenized chat so far, including control tokens. This * **output is ready to pass to the model, either directly or via methods like `generate()`. If `return_dict` is** * **set, will return a dict of tokenizer outputs instead.** .. py:method:: get_chat_template(chat_template = None, tools = None) Retrieve the chat template string used for tokenizing chat messages. This template is used internally by the `apply_chat_template` method and can also be used externally to retrieve the model's chat template for better generation tracking. :param chat_template: A Jinja template or the name of a template to use for this conversion. It is usually not necessary to pass anything to this argument, as the model's template will be used by default. :type chat_template: `str`, *optional* :param tools: A list of tools (callable functions) that will be accessible to the model. If the template does not support function calling, this argument will have no effect. Each tool should be passed as a JSON Schema, giving the name, description and argument types for the tool. See our [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#automated-function-conversion-for-tool-use) for more information. :type tools: `List[Dict]`, *optional* :Returns: **`str`** -- The chat template string. .. py:method:: from_pretrained(pretrained_model_name_or_path, *init_inputs, cache_dir = None, force_download = False, local_files_only = False, token = None, revision = 'main', trust_remote_code=False, **kwargs) :classmethod: Instantiate a [`~tokenization_utils_base.PreTrainedTokenizerBase`] (or a derived class) from a predefined tokenizer. :param pretrained_model_name_or_path: Can be either: - A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co. - A path to a *directory* containing vocabulary files required by the tokenizer, for instance saved using the [`~tokenization_utils_base.PreTrainedTokenizerBase.save_pretrained`] method, e.g., `./my_model_directory/`. - (**Deprecated**, not applicable to all derived classes) A path or url to a single saved vocabulary file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g., `./my_model_directory/vocab.txt`. :type pretrained_model_name_or_path: `str` or `os.PathLike` :param cache_dir: Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used. :type cache_dir: `str` or `os.PathLike`, *optional* :param force_download: Whether or not to force the (re-)download the vocabulary files and override the cached versions if they exist. :type force_download: `bool`, *optional*, defaults to `False` :param resume_download: Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers. :param proxies: A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. :type proxies: `Dict[str, str]`, *optional* :param token: The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). :type token: `str` or *bool*, *optional* :param local_files_only: Whether or not to only rely on local files and not to attempt to download any files. :type local_files_only: `bool`, *optional*, defaults to `False` :param revision: The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. :type revision: `str`, *optional*, defaults to `"main"` :param subfolder: In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for facebook/rag-token-base), specify it here. :type subfolder: `str`, *optional* :param inputs: Will be passed along to the Tokenizer `__init__` method. :type inputs: additional positional arguments, *optional* :param trust_remote_code: Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. :type trust_remote_code: `bool`, *optional*, defaults to `False` :param kwargs: Will be passed to the Tokenizer `__init__` method. Can be used to set special tokens like `bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`. See parameters in the `__init__` for more details. :type kwargs: additional keyword arguments, *optional* Passing `token=True` is required when you want to use a private model. Examples: ```python # We can't instantiate directly the base class *PreTrainedTokenizerBase* so let's show our examples on a derived class: BertTokenizer # Download vocabulary from huggingface.co and cache. tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased") # Download vocabulary from huggingface.co (user-uploaded) and cache. tokenizer = BertTokenizer.from_pretrained("dbmdz/bert-base-german-cased") # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*) tokenizer = BertTokenizer.from_pretrained("./test/saved_model/") # If the tokenizer uses a single vocabulary file, you can point directly to this file tokenizer = BertTokenizer.from_pretrained("./test/saved_model/my_vocab.txt") # You can link tokens to special vocabulary when instantiating tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased", unk_token="") # You should be sure '' is in the vocabulary when doing that. # Otherwise use tokenizer.add_special_tokens({'unk_token': ''}) instead) assert tokenizer.unk_token == "" ``` .. py:method:: _from_pretrained(resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, token=None, cache_dir=None, local_files_only=False, _commit_hash=None, _is_local=False, trust_remote_code=False, **kwargs) :classmethod: .. py:method:: _eventually_correct_t5_max_length(pretrained_model_name_or_path, max_model_length, init_max_model_length) :staticmethod: .. py:method:: convert_added_tokens(obj, save=False, add_type_field=True) :classmethod: .. py:method:: save_pretrained(save_directory, legacy_format = None, filename_prefix = None, push_to_hub = False, **kwargs) Save the full tokenizer state. This method make sure the full tokenizer can then be re-loaded using the [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`] class method.. Warning,None This won't save modifications you may have applied to the tokenizer after the instantiation (for instance, modifying `tokenizer.do_lower_case` after creation). :param save_directory: The path to a directory where the tokenizer will be saved. :type save_directory: `str` or `os.PathLike` :param legacy_format: Only applicable for a fast tokenizer. If unset (default), will save the tokenizer in the unified JSON format as well as in legacy format if it exists, i.e. with tokenizer specific vocabulary and a separate added_tokens files. If `False`, will only save the tokenizer in the unified JSON format. This format is incompatible with "slow" tokenizers (not powered by the *tokenizers* library), so the tokenizer will not be able to be loaded in the corresponding "slow" tokenizer. If `True`, will save the tokenizer in legacy format. If the "slow" tokenizer doesn't exits, a value error is raised. :type legacy_format: `bool`, *optional* :param filename_prefix: A prefix to add to the names of the files saved by the tokenizer. :type filename_prefix: `str`, *optional* :param push_to_hub: Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). :type push_to_hub: `bool`, *optional*, defaults to `False` :param kwargs: Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. :type kwargs: `Dict[str, Any]`, *optional* :Returns: **A tuple of `str`** -- The files saved. .. py:method:: save_vocabulary(save_directory, filename_prefix = None) :abstractmethod: Save only the vocabulary of the tokenizer (vocabulary + added tokens). This method won't save the configuration and special token mappings of the tokenizer. Use [`~PreTrainedTokenizerFast._save_pretrained`] to save the whole state of the tokenizer. :param save_directory: The directory in which to save the vocabulary. :type save_directory: `str` :param filename_prefix: An optional prefix to add to the named of the saved files. :type filename_prefix: `str`, *optional* :Returns: **`Tuple** (*str*) -- Paths to the files saved. .. py:method:: encode(text, text_pair = None, add_special_tokens = True, padding = False, truncation = None, max_length = None, stride = 0, padding_side = None, return_tensors = None, **kwargs) Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary. Same as doing `self.convert_tokens_to_ids(self.tokenize(text))`. :param text: The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method). :type text: `str`, `List[str]` or `List[int]` :param text_pair: Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method). :type text_pair: `str`, `List[str]` or `List[int]`, *optional* .. py:method:: _get_padding_truncation_strategies(padding=False, truncation=None, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs) Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy and pad_to_max_length) and behaviors. .. py:method:: _call_one(text, text_pair = None, add_special_tokens = True, padding = False, truncation = None, max_length = None, stride = 0, is_split_into_words = False, pad_to_multiple_of = None, padding_side = None, return_tensors = None, return_token_type_ids = None, return_attention_mask = None, return_overflowing_tokens = False, return_special_tokens_mask = False, return_offsets_mapping = False, return_length = False, verbose = True, split_special_tokens = False, **kwargs) .. py:method:: encode_plus(text, text_pair = None, add_special_tokens = True, padding = False, truncation = None, max_length = None, stride = 0, is_split_into_words = False, pad_to_multiple_of = None, padding_side = None, return_tensors = None, return_token_type_ids = None, return_attention_mask = None, return_overflowing_tokens = False, return_special_tokens_mask = False, return_offsets_mapping = False, return_length = False, verbose = True, **kwargs) Tokenize and prepare for the model a sequence or a pair of sequences. This method is deprecated, `__call__` should be used instead. :param text: The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method). :type text: `str`, `List[str]` or (for non-fast tokenizers) `List[int]` :param text_pair: Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method). :type text_pair: `str`, `List[str]` or `List[int]`, *optional* .. py:method:: batch_encode_plus(batch_text_or_text_pairs, add_special_tokens = True, padding = False, truncation = None, max_length = None, stride = 0, is_split_into_words = False, pad_to_multiple_of = None, padding_side = None, return_tensors = None, return_token_type_ids = None, return_attention_mask = None, return_overflowing_tokens = False, return_special_tokens_mask = False, return_offsets_mapping = False, return_length = False, verbose = True, split_special_tokens = False, **kwargs) Tokenize and prepare for the model a list of sequences or a list of pairs of sequences. This method is deprecated, `__call__` should be used instead. :param batch_text_or_text_pairs: Batch of sequences or pair of sequences to be encoded. This can be a list of string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see details in `encode_plus`). :type batch_text_or_text_pairs: `List[str]`, `List[Tuple[str, str]]`, `List[List[str]]`, `List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also `List[List[int]]`, `List[Tuple[List[int], List[int]]]` .. py:method:: pad(encoded_inputs, padding = True, max_length = None, pad_to_multiple_of = None, padding_side = None, return_attention_mask = None, return_tensors = None, verbose = True) Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`). Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the specific device of your tensors however. :param encoded_inputs: Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str, List[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader collate function. Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type. :type encoded_inputs: [`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]` :param padding: Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). :type padding: `bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True` :param max_length: Maximum length of the returned list and optionally padding length (see above). :type max_length: `int`, *optional* :param pad_to_multiple_of: If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). :type pad_to_multiple_of: `int`, *optional* :param padding_side: The side on which the model should have padding applied. Should be selected between ['right', 'left']. Default value is picked from the class attribute of the same name. :type padding_side: `str`, *optional* :param return_attention_mask: Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) :type return_attention_mask: `bool`, *optional* :param return_tensors: If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. :type return_tensors: `str` or [`~utils.TensorType`], *optional* :param verbose: Whether or not to print more information and warnings. :type verbose: `bool`, *optional*, defaults to `True` .. py:method:: create_token_type_ids_from_sequences(token_ids_0, token_ids_1 = None) Create the token type IDs corresponding to the sequences passed. [What are token type IDs?](../glossary#token-type-ids) Should be overridden in a subclass if the model has a special way of building those. :param token_ids_0: The first tokenized sequence. :type token_ids_0: `List[int]` :param token_ids_1: The second tokenized sequence. :type token_ids_1: `List[int]`, *optional* :Returns: **`List[int]`** -- The token type ids. .. py:method:: build_inputs_with_special_tokens(token_ids_0, token_ids_1 = None) Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. This implementation does not add special tokens and this method should be overridden in a subclass. :param token_ids_0: The first tokenized sequence. :type token_ids_0: `List[int]` :param token_ids_1: The second tokenized sequence. :type token_ids_1: `List[int]`, *optional* :Returns: **`List[int]`** -- The model input with special tokens. .. py:method:: prepare_for_model(ids, pair_ids = None, add_special_tokens = True, padding = False, truncation = None, max_length = None, stride = 0, pad_to_multiple_of = None, padding_side = None, return_tensors = None, return_token_type_ids = None, return_attention_mask = None, return_overflowing_tokens = False, return_special_tokens_mask = False, return_offsets_mapping = False, return_length = False, verbose = True, prepend_batch_axis = False, **kwargs) Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. :param ids: Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. :type ids: `List[int]` :param pair_ids: Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. :type pair_ids: `List[int]`, *optional* .. py:method:: truncate_sequences(ids, pair_ids = None, num_tokens_to_remove = 0, truncation_strategy = 'longest_first', stride = 0) Truncates a sequence pair in-place following the strategy. :param ids: Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. :type ids: `List[int]` :param pair_ids: Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. :type pair_ids: `List[int]`, *optional* :param num_tokens_to_remove: Number of tokens to remove using the truncation strategy. :type num_tokens_to_remove: `int`, *optional*, defaults to 0 :param truncation_strategy: The strategy to follow for truncation. Can be: - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). :type truncation_strategy: `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `'longest_first'` :param stride: If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens. :type stride: `int`, *optional*, defaults to 0 :Returns: * **`Tuple[List[int], List[int], List[int]]`** -- The truncated `ids`, the truncated `pair_ids` and the list of * **overflowing tokens. Note** -- The *longest_first* strategy returns empty list of overflowing tokens if a pair * **of sequences** (*or a batch of pairs*) .. py:method:: _pad(encoded_inputs, max_length = None, padding_strategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of = None, padding_side = None, return_attention_mask = None) Pad encoded inputs (on left/right and up to predefined length or max length in the batch) :param encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). :param max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. :param padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in `padding_side` argument: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences :param pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability `>= 7.5` (Volta). :param padding_side: The side on which the model should have padding applied. Should be selected between ['right', 'left']. Default value is picked from the class attribute of the same name. :param return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) .. py:method:: batch_decode(sequences, skip_special_tokens = False, clean_up_tokenization_spaces = None, **kwargs) Convert a list of lists of token ids into a list of strings by calling decode. :param sequences: List of tokenized input ids. Can be obtained using the `__call__` method. :type sequences: `Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]` :param skip_special_tokens: Whether or not to remove special tokens in the decoding. :type skip_special_tokens: `bool`, *optional*, defaults to `False` :param clean_up_tokenization_spaces: Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces`. :type clean_up_tokenization_spaces: `bool`, *optional* :param kwargs: Will be passed to the underlying model specific decode method. :type kwargs: additional keyword arguments, *optional* :Returns: **`List[str]`** -- The list of decoded sentences. .. py:method:: decode(token_ids, skip_special_tokens = False, clean_up_tokenization_spaces = None, **kwargs) Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. :param token_ids: List of tokenized input ids. Can be obtained using the `__call__` method. :type token_ids: `Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]` :param skip_special_tokens: Whether or not to remove special tokens in the decoding. :type skip_special_tokens: `bool`, *optional*, defaults to `False` :param clean_up_tokenization_spaces: Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces`. :type clean_up_tokenization_spaces: `bool`, *optional* :param kwargs: Will be passed to the underlying model specific decode method. :type kwargs: additional keyword arguments, *optional* :Returns: **`str`** -- The decoded sentence. .. py:method:: get_special_tokens_mask(token_ids_0, token_ids_1 = None, already_has_special_tokens = False) Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods. :param token_ids_0: List of ids of the first sequence. :type token_ids_0: `List[int]` :param token_ids_1: List of ids of the second sequence. :type token_ids_1: `List[int]`, *optional* :param already_has_special_tokens: Whether or not the token list is already formatted with special tokens for the model. :type already_has_special_tokens: `bool`, *optional*, defaults to `False` :Returns: **A list of integers in the range [0, 1]** -- 1 for a special token, 0 for a sequence token. .. py:method:: clean_up_tokenization(out_string) :staticmethod: Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms. :param out_string: The text to clean up. :type out_string: `str` :Returns: **`str`** -- The cleaned-up string. .. py:method:: _eventual_warn_about_too_long_sequence(ids, max_length, verbose) Depending on the input and internal state we might trigger a warning about a sequence that is too long for its corresponding model :param ids: The ids produced by the tokenization :type ids: `List[str]` :param max_length: The max_length desired (does not trigger a warning if it is set) :type max_length: `int`, *optional* :param verbose: Whether or not to print more information and warnings. :type verbose: `bool` .. py:method:: _switch_to_input_mode() Private method to put the tokenizer in input mode (when it has different modes for input/outputs) .. py:method:: _switch_to_target_mode() Private method to put the tokenizer in target mode (when it has different modes for input/outputs) .. py:method:: as_target_tokenizer() Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels. .. py:method:: register_for_auto_class(auto_class='AutoTokenizer') :classmethod: Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the library are already mapped with `AutoTokenizer`. This API is experimental and may have some slight breaking changes in the next releases. :param auto_class: The auto class to register this new tokenizer with. :type auto_class: `str` or `type`, *optional*, defaults to `"AutoTokenizer"` .. py:method:: prepare_seq2seq_batch(src_texts, tgt_texts = None, max_length = None, max_target_length = None, padding = 'longest', return_tensors = None, truncation = True, **kwargs) Prepare model inputs for translation. For best performance, translate one sentence at a time. :param src_texts: List of documents to summarize or source language texts. :type src_texts: `List[str]` :param tgt_texts: List of summaries or target language texts. :type tgt_texts: `list`, *optional* :param max_length: Controls the maximum length for encoder inputs (documents to summarize or source language texts) If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. :type max_length: `int`, *optional* :param max_target_length: Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set to `None`, this will use the max_length value. :type max_target_length: `int`, *optional* :param padding: Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). :type padding: `bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False` :param return_tensors: If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. :type return_tensors: `str` or [`~utils.TensorType`], *optional* :param truncation: Activates and controls truncation. Accepts the following values: - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). :type truncation: `bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `True` :param \*\*kwargs: Additional keyword arguments passed along to `self.__call__`. :returns: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to the encoder. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model. - **labels** -- List of token ids for tgt_texts. The full set of keys `[input_ids, attention_mask, labels]`, will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys. :rtype: [`BatchEncoding`] .. py:attribute:: SPECIAL_TOKENS_ATTRIBUTES :value: ['bos_token', 'eos_token', 'unk_token', 'sep_token', 'pad_token', 'cls_token', 'mask_token',... .. py:attribute:: _pad_token_type_id :value: 0 .. py:attribute:: verbose :value: False .. py:attribute:: _special_tokens_map .. py:method:: sanitize_special_tokens() The `sanitize_special_tokens` is now deprecated kept for backward compatibility and will be removed in transformers v5. .. py:method:: add_special_tokens(special_tokens_dict, replace_additional_special_tokens=True) Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the current vocabulary). When adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of the model so that its embedding matrix matches the tokenizer. In order to do that, please use the [`~PreTrainedModel.resize_token_embeddings`] method. Using `add_special_tokens` will ensure your special tokens can be used in several ways: - Special tokens can be skipped when decoding using `skip_special_tokens = True`. - Special tokens are carefully handled by the tokenizer (they are never split), similar to `AddedTokens`. - You can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts. When possible, special tokens are already registered for provided pretrained models (for instance [`BertTokenizer`] `cls_token` is already registered to be :obj*'[CLS]'* and XLM's one is also registered to be `''`). :param special_tokens_dict: Keys should be in the list of predefined special attributes: [`bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`]. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the `unk_token` to them). :type special_tokens_dict: dictionary *str* to *str* or `tokenizers.AddedToken` :param replace_additional_special_tokens: If `True`, the existing list of additional special tokens will be replaced by the list provided in `special_tokens_dict`. Otherwise, `self._special_tokens_map["additional_special_tokens"]` is just extended. In the former case, the tokens will NOT be removed from the tokenizer's full vocabulary - they are only being flagged as non-special tokens. Remember, this only affects which tokens are skipped during decoding, not the `added_tokens_encoder` and `added_tokens_decoder`. This means that the previous `additional_special_tokens` are still added tokens, and will not be split by the model. :type replace_additional_special_tokens: `bool`, *optional*,, defaults to `True` :Returns: **`int`** -- Number of tokens added to the vocabulary. Examples: ```python # Let's see how to add a new classification token to GPT-2 tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") model = GPT2Model.from_pretrained("openai-community/gpt2") special_tokens_dict = {"cls_token": ""} num_added_toks = tokenizer.add_special_tokens(special_tokens_dict) print("We have added", num_added_toks, "tokens") # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) assert tokenizer.cls_token == "" ``` .. py:method:: add_tokens(new_tokens, special_tokens = False) Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary and will be isolated before the tokenization algorithm is applied. Added tokens and tokens from the vocabulary of the tokenization algorithm are therefore not treated in the same way. Note, when adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of the model so that its embedding matrix matches the tokenizer. In order to do that, please use the [`~PreTrainedModel.resize_token_embeddings`] method. :param new_tokens: Tokens are only added if they are not already in the vocabulary. `tokenizers.AddedToken` wraps a string token to let you personalize its behavior: whether this token should only match against a single word, whether this token should strip all potential whitespaces on the left side, whether this token should strip all potential whitespaces on the right side, etc. :type new_tokens: `str`, `tokenizers.AddedToken` or a list of *str* or `tokenizers.AddedToken` :param special_tokens: Can be used to specify if the token is a special token. This mostly change the normalization behavior (special tokens like CLS or [MASK] are usually not lower-cased for instance). See details for `tokenizers.AddedToken` in HuggingFace tokenizers library. :type special_tokens: `bool`, *optional*, defaults to `False` :Returns: **`int`** -- Number of tokens added to the vocabulary. Examples: ```python # Let's see how to increase the vocabulary of Bert model and tokenizer tokenizer = BertTokenizerFast.from_pretrained("google-bert/bert-base-uncased") model = BertModel.from_pretrained("google-bert/bert-base-uncased") num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"]) print("We have added", num_added_toks, "tokens") # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) ``` .. py:property:: pad_token_type_id :type: int Id of the padding token type in the vocabulary. :type: `int` .. py:method:: __setattr__(key, value) .. py:method:: __getattr__(key) .. py:property:: special_tokens_map :type: Dict[str, Union[str, List[str]]] A dictionary mapping special token class attributes (`cls_token`, `unk_token`, etc.) to their values (`''`, `''`, etc.). Convert potential tokens of `tokenizers.AddedToken` type to string. :type: `Dict[str, Union[str, List[str]]]` .. py:property:: special_tokens_map_extended :type: Dict[str, Union[str, tokenizers.AddedToken, List[Union[str, tokenizers.AddedToken]]]] A dictionary mapping special token class attributes (`cls_token`, `unk_token`, etc.) to their values (`''`, `''`, etc.). Don't convert tokens of `tokenizers.AddedToken` type to string so they can be used to control more finely how special tokens are tokenized. :type: `Dict[str, Union[str, tokenizers.AddedToken, List[Union[str, tokenizers.AddedToken]]]]` .. py:property:: all_special_tokens_extended :type: List[Union[str, tokenizers.AddedToken]] All the special tokens (`''`, `''`, etc.), the order has nothing to do with the index of each tokens. If you want to know the correct indices, check `self.added_tokens_encoder`. We can't create an order anymore as the keys are `AddedTokens` and not `Strings`. Don't convert tokens of `tokenizers.AddedToken` type to string so they can be used to control more finely how special tokens are tokenized. :type: `List[Union[str, tokenizers.AddedToken]]` .. py:property:: all_special_tokens :type: List[str] A list of the unique special tokens (`''`, `''`, ..., etc.). Convert tokens of `tokenizers.AddedToken` type to string. :type: `List[str]` .. py:property:: all_special_ids :type: List[int] List the ids of the special tokens(`''`, `''`, etc.) mapped to class attributes. :type: `List[int]` .. py:method:: _set_model_specific_special_tokens(special_tokens) Adds new special tokens to the "SPECIAL_TOKENS_ATTRIBUTES" list which will be part of "self.special_tokens" and saved as a special token in tokenizer's config. This allows us to dynamically add new model-type specific tokens after initilizing the tokenizer. For example: if the model tokenizers is multimodal, we can support special image or audio tokens. .. py:method:: _create_repo(repo_id, private = None, token = None, repo_url = None, organization = None) Create the repo if needed, cleans up repo_id with deprecated kwargs `repo_url` and `organization`, retrieves the token. .. py:method:: _get_files_timestamps(working_dir) Returns the list of files with their last modification timestamp. .. py:method:: _upload_modified_files(working_dir, repo_id, files_timestamps, commit_message = None, token = None, create_pr = False, revision = None, commit_description = None) Uploads all modified files in `working_dir` to `repo_id`, based on `files_timestamps`. .. py:method:: push_to_hub(repo_id, use_temp_dir = None, commit_message = None, private = None, token = None, max_shard_size = '5GB', create_pr = False, safe_serialization = True, revision = None, commit_description = None, tags = None, **deprecated_kwargs) Upload the {object_files} to the 🤗 Model Hub. :param repo_id: The name of the repository you want to push your {object} to. It should contain your organization name when pushing to a given organization. :type repo_id: `str` :param use_temp_dir: Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to `True` if there is no directory named like `repo_id`, `False` otherwise. :type use_temp_dir: `bool`, *optional* :param commit_message: Message to commit while pushing. Will default to `"Upload {object}"`. :type commit_message: `str`, *optional* :param private: Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists. :type private: `bool`, *optional* :param token: The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url` is not specified. :type token: `bool` or `str`, *optional* :param max_shard_size: Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`). We default it to `"5GB"` so that users can easily load models on free-tier Google Colab instances without any CPU OOM issues. :type max_shard_size: `int` or `str`, *optional*, defaults to `"5GB"` :param create_pr: Whether or not to create a PR with the uploaded files or directly commit. :type create_pr: `bool`, *optional*, defaults to `False` :param safe_serialization: Whether or not to convert the model weights in safetensors format for safer serialization. :type safe_serialization: `bool`, *optional*, defaults to `True` :param revision: Branch to push the uploaded files to. :type revision: `str`, *optional* :param commit_description: The description of the commit that will be created :type commit_description: `str`, *optional* :param tags: List of tags to push on the Hub. :type tags: `List[str]`, *optional* Examples: ```python from transformers import {object_class} {object} = {object_class}.from_pretrained("google-bert/bert-base-cased") # Push the {object} to your namespace with the name "my-finetuned-bert". {object}.push_to_hub("my-finetuned-bert") # Push the {object} to an organization with the name "my-finetuned-bert". {object}.push_to_hub("huggingface/my-finetuned-bert") ``` .. py:class:: RelExtrBaseConfig(pretrained_model_name_or_path, **kwargs) Bases: :py:obj:`transformers.PretrainedConfig` Base class for the RelCAT models .. py:attribute:: name :value: 'base-config-relcat' .. py:method:: __init__(pretrained_model_name_or_path, **kwargs) .. py:attribute:: model_type :value: 'relcat' .. py:attribute:: pretrained_model_name_or_path .. py:attribute:: hf_model_config :type: transformers.PretrainedConfig .. py:method:: to_dict() Serializes this instance to a Python dictionary. :Returns: **`Dict[str, Any]`** -- Dictionary of all the attributes that make up this configuration instance. .. py:method:: save(save_path) .. py:method:: load(pretrained_model_name_or_path, relcat_config, **kwargs) :classmethod: .. py:attribute:: base_config_key :type: str :value: '' .. py:attribute:: sub_configs :type: Dict[str, PretrainedConfig] .. py:attribute:: is_composition :type: bool :value: False .. py:attribute:: attribute_map :type: Dict[str, str] .. py:attribute:: base_model_tp_plan :type: Optional[Dict[str, Any]] :value: None .. py:attribute:: _auto_class :type: Optional[str] :value: None .. py:method:: __setattr__(key, value) .. py:method:: __getattribute__(key) .. py:attribute:: return_dict .. py:attribute:: output_hidden_states .. py:attribute:: output_attentions .. py:attribute:: torchscript .. py:attribute:: torch_dtype .. py:attribute:: use_bfloat16 .. py:attribute:: tf_legacy_loss .. py:attribute:: pruned_heads .. py:attribute:: tie_word_embeddings .. py:attribute:: chunk_size_feed_forward .. py:attribute:: is_encoder_decoder .. py:attribute:: is_decoder .. py:attribute:: cross_attention_hidden_size .. py:attribute:: add_cross_attention .. py:attribute:: tie_encoder_decoder .. py:attribute:: architectures .. py:attribute:: finetuning_task .. py:attribute:: id2label .. py:attribute:: label2id .. py:attribute:: tokenizer_class .. py:attribute:: prefix .. py:attribute:: bos_token_id .. py:attribute:: pad_token_id .. py:attribute:: eos_token_id .. py:attribute:: sep_token_id .. py:attribute:: decoder_start_token_id .. py:attribute:: task_specific_params .. py:attribute:: problem_type .. py:attribute:: _name_or_path :value: '' .. py:attribute:: _commit_hash .. py:attribute:: _attn_implementation_internal .. py:attribute:: _attn_implementation_autoset :value: False .. py:attribute:: transformers_version .. py:property:: name_or_path :type: str .. py:property:: use_return_dict :type: bool Whether or not return [`~utils.ModelOutput`] instead of tuples. :type: `bool` .. py:property:: num_labels :type: int The number of labels for classification models. :type: `int` .. py:property:: _attn_implementation .. py:method:: save_pretrained(save_directory, push_to_hub = False, **kwargs) Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the [`~PretrainedConfig.from_pretrained`] class method. :param save_directory: Directory where the configuration JSON file will be saved (will be created if it does not exist). :type save_directory: `str` or `os.PathLike` :param push_to_hub: Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). :type push_to_hub: `bool`, *optional*, defaults to `False` :param kwargs: Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. :type kwargs: `Dict[str, Any]`, *optional* .. py:method:: _set_token_in_kwargs(kwargs, token=None) :staticmethod: Temporary method to deal with `token` and `use_auth_token`. This method is to avoid apply the same changes in all model config classes that overwrite `from_pretrained`. Need to clean up `use_auth_token` in a follow PR. .. py:method:: from_pretrained(pretrained_model_name_or_path, cache_dir = None, force_download = False, local_files_only = False, token = None, revision = 'main', **kwargs) :classmethod: Instantiate a [`PretrainedConfig`] (or a derived class) from a pretrained model configuration. :param pretrained_model_name_or_path: This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. - a path to a *directory* containing a configuration file saved using the [`~PretrainedConfig.save_pretrained`] method, e.g., `./my_model_directory/`. - a path or url to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`. :type pretrained_model_name_or_path: `str` or `os.PathLike` :param cache_dir: Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. :type cache_dir: `str` or `os.PathLike`, *optional* :param force_download: Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. :type force_download: `bool`, *optional*, defaults to `False` :param resume_download: Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers. :param proxies: A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. :type proxies: `Dict[str, str]`, *optional* :param token: The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). :type token: `str` or `bool`, *optional* :param revision: The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`. :type revision: `str`, *optional*, defaults to `"main"` :param return_unused_kwargs: If `False`, then this function returns just the final configuration object. If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the part of `kwargs` which has not been used to update `config` and is otherwise ignored. :type return_unused_kwargs: `bool`, *optional*, defaults to `False` :param subfolder: In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. :type subfolder: `str`, *optional*, defaults to `""` :param kwargs: The values in kwargs of any keys which are configuration attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter. :type kwargs: `Dict[str, Any]`, *optional* :Returns: **[`PretrainedConfig`]** -- The configuration object instantiated from this pretrained model. Examples: ```python # We can't instantiate directly the base class *PretrainedConfig* so let's show the examples on a # derived class: BertConfig config = BertConfig.from_pretrained( "google-bert/bert-base-uncased" ) # Download configuration from huggingface.co and cache. config = BertConfig.from_pretrained( "./test/saved_model/" ) # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')* config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json") config = BertConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False) assert config.output_attentions == True config, unused_kwargs = BertConfig.from_pretrained( "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True ) assert config.output_attentions == True assert unused_kwargs == {"foo": False} ``` .. py:method:: get_config_dict(pretrained_model_name_or_path, **kwargs) :classmethod: From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a [`PretrainedConfig`] using `from_dict`. :param pretrained_model_name_or_path: The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. :type pretrained_model_name_or_path: `str` or `os.PathLike` :Returns: **`Tuple[Dict, Dict]`** -- The dictionary(ies) that will be used to instantiate the configuration object. .. py:method:: _get_config_dict(pretrained_model_name_or_path, **kwargs) :classmethod: .. py:method:: from_dict(config_dict, **kwargs) :classmethod: Instantiates a [`PretrainedConfig`] from a Python dictionary of parameters. :param config_dict: Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~PretrainedConfig.get_config_dict`] method. :type config_dict: `Dict[str, Any]` :param kwargs: Additional parameters from which to initialize the configuration object. :type kwargs: `Dict[str, Any]` :Returns: **[`PretrainedConfig`]** -- The configuration object instantiated from those parameters. .. py:method:: from_json_file(json_file) :classmethod: Instantiates a [`PretrainedConfig`] from the path to a JSON file of parameters. :param json_file: Path to the JSON file containing the parameters. :type json_file: `str` or `os.PathLike` :Returns: **[`PretrainedConfig`]** -- The configuration object instantiated from that JSON file. .. py:method:: _dict_from_json_file(json_file) :classmethod: .. py:method:: __eq__(other) .. py:method:: __repr__() .. py:method:: __iter__() .. py:method:: to_diff_dict() Removes all attributes from config which correspond to the default config attributes for better readability and serializes to a Python dictionary. :Returns: **`Dict[str, Any]`** -- Dictionary of all the attributes that make up this configuration instance, .. py:method:: to_json_string(use_diff = True) Serializes this instance to a JSON string. :param use_diff: If set to `True`, only the difference between the config instance and the default `PretrainedConfig()` is serialized to JSON string. :type use_diff: `bool`, *optional*, defaults to `True` :Returns: **`str`** -- String containing all the attributes that make up this configuration instance in JSON format. .. py:method:: to_json_file(json_file_path, use_diff = True) Save this instance to a JSON file. :param json_file_path: Path to the JSON file in which this configuration instance's parameters will be saved. :type json_file_path: `str` or `os.PathLike` :param use_diff: If set to `True`, only the difference between the config instance and the default `PretrainedConfig()` is serialized to JSON file. :type use_diff: `bool`, *optional*, defaults to `True` .. py:method:: update(config_dict) Updates attributes of this class with attributes from `config_dict`. :param config_dict: Dictionary of attributes that should be updated for this class. :type config_dict: `Dict[str, Any]` .. py:method:: update_from_string(update_str) Updates attributes of this class with attributes from `update_str`. The expected format is ints, floats and strings as is, and for booleans use `true` or `false`. For example: "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" The keys to change have to already exist in the config object. :param update_str: String with attributes that should be updated for this class. :type update_str: `str` .. py:method:: dict_torch_dtype_to_str(d) Checks whether the passed dictionary and its nested dicts have a *torch_dtype* key and if it's not None, converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"* string, which can then be stored in the json format. .. py:method:: register_for_auto_class(auto_class='AutoConfig') :classmethod: Register this class with a given auto class. This should only be used for custom configurations as the ones in the library are already mapped with `AutoConfig`. This API is experimental and may have some slight breaking changes in the next releases. :param auto_class: The auto class to register this new configuration with. :type auto_class: `str` or `type`, *optional*, defaults to `"AutoConfig"` .. py:method:: _get_global_generation_defaults() :staticmethod: .. py:method:: _get_non_default_generation_parameters() Gets the non-default generation parameters on the PretrainedConfig instance .. py:method:: get_text_config(decoder=False) Returns the config that is meant to be used with text IO. On most models, it is the original config instance itself. On specific composite models, it is under a set of valid names. If `decoder` is set to `True`, then only search for decoder config names. .. py:method:: _create_repo(repo_id, private = None, token = None, repo_url = None, organization = None) Create the repo if needed, cleans up repo_id with deprecated kwargs `repo_url` and `organization`, retrieves the token. .. py:method:: _get_files_timestamps(working_dir) Returns the list of files with their last modification timestamp. .. py:method:: _upload_modified_files(working_dir, repo_id, files_timestamps, commit_message = None, token = None, create_pr = False, revision = None, commit_description = None) Uploads all modified files in `working_dir` to `repo_id`, based on `files_timestamps`. .. py:method:: push_to_hub(repo_id, use_temp_dir = None, commit_message = None, private = None, token = None, max_shard_size = '5GB', create_pr = False, safe_serialization = True, revision = None, commit_description = None, tags = None, **deprecated_kwargs) Upload the {object_files} to the 🤗 Model Hub. :param repo_id: The name of the repository you want to push your {object} to. It should contain your organization name when pushing to a given organization. :type repo_id: `str` :param use_temp_dir: Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to `True` if there is no directory named like `repo_id`, `False` otherwise. :type use_temp_dir: `bool`, *optional* :param commit_message: Message to commit while pushing. Will default to `"Upload {object}"`. :type commit_message: `str`, *optional* :param private: Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists. :type private: `bool`, *optional* :param token: The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url` is not specified. :type token: `bool` or `str`, *optional* :param max_shard_size: Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`). We default it to `"5GB"` so that users can easily load models on free-tier Google Colab instances without any CPU OOM issues. :type max_shard_size: `int` or `str`, *optional*, defaults to `"5GB"` :param create_pr: Whether or not to create a PR with the uploaded files or directly commit. :type create_pr: `bool`, *optional*, defaults to `False` :param safe_serialization: Whether or not to convert the model weights in safetensors format for safer serialization. :type safe_serialization: `bool`, *optional*, defaults to `True` :param revision: Branch to push the uploaded files to. :type revision: `str`, *optional* :param commit_description: The description of the commit that will be created :type commit_description: `str`, *optional* :param tags: List of tags to push on the Hub. :type tags: `List[str]`, *optional* Examples: ```python from transformers import {object_class} {object} = {object_class}.from_pretrained("google-bert/bert-base-cased") # Push the {object} to your namespace with the name "my-finetuned-bert". {object}.push_to_hub("my-finetuned-bert") # Push the {object} to an organization with the name "my-finetuned-bert". {object}.push_to_hub("huggingface/my-finetuned-bert") ``` .. py:function:: load_state(model, optimizer, scheduler, path = './', model_name = 'BERT', file_prefix = 'train', load_best = False, relcat_config = ConfigRelCAT()) Used by RelCAT.load() and RelCAT.train() :param model: RelExtrBaseModel, it has to be initialized before calling this method via RelExtr(Bert/Llama)Model(...) :type model: RelExtrBaseModel :param optimizer: optimizer :type optimizer: _type_ :param scheduler: scheduler :type scheduler: _type_ :param path: Defaults to "./". :type path: str, optional :param model_name: Defaults to "BERT". :type model_name: str, optional :param file_prefix: Defaults to "train". :type file_prefix: str, optional :param load_best: Defaults to False. :type load_best: bool, optional :param relcat_config: Defaults to ConfigRelCAT(). :type relcat_config: ConfigRelCAT :Returns: **tuple** (*int, int*) -- last epoch and f1 score. .. py:function:: save_state(model, optimizer, scheduler, epoch = 1, best_f1 = 0.0, path = './', model_name = 'BERT', task = 'train', is_checkpoint=False, final_export=False) Used by RelCAT.save() and RelCAT.train() Saves the RelCAT model state. For checkpointing multiple files are created, best_f1, loss etc. score. If you want to export the model after training set final_export=True and leave is_checkpoint=False. :param model: BertMode | LlamaModel etc. :type model: BaseModel :param optimizer: Defaults to None. :type optimizer: torch.optim.AdamW, optional :param scheduler: Defaults to None. :type scheduler: torch.optim.lr_scheduler.MultiStepLR, optional :param epoch: Defaults to None. :type epoch: int :param best_f1: Defaults to None. :type best_f1: float :param path: Defaults to "./". :type path: str :param model_name: . Defaults to "BERT". This is used to checkpointing only. :type model_name: str :param task: Defaults to "train". This is used to checkpointing only. :type task: str :param is_checkpoint: Defaults to False. :type is_checkpoint: bool :param final_export: Defaults to False, if True then is_checkpoint must be False also. Exports model.state_dict(), out into "model.dat". :type final_export: bool .. py:data:: logger .. py:class:: RelExtrBaseComponent(tokenizer = BaseTokenizerWrapper(), model = None, model_config = None, config = ConfigRelCAT(), task = 'train', init_model = False) .. py:attribute:: name :value: 'base_component_rel' .. py:method:: __init__(tokenizer = BaseTokenizerWrapper(), model = None, model_config = None, config = ConfigRelCAT(), task = 'train', init_model = False) Component that holds the model and everything for RelCAT. :param tokenizer: The base tokenizer for RelCAT. :type tokenizer: BaseTokenizerWrapper :param model: The model wrapper. :type model: RelExtrBaseModel :param model_config: The model-specific config. :type model_config: RelExtrBaseConfig :param config: The RelCAT config. :type config: ConfigRelCAT :param task: The task - used for checkpointing. :type task: str :param init_model: Loads default BERT base model, tokenizer, model config. Defaults to False. :type init_model: bool .. py:attribute:: model :type: medcat.components.addons.relation_extraction.models.RelExtrBaseModel :value: None .. py:attribute:: tokenizer :type: medcat.components.addons.relation_extraction.tokenizer.BaseTokenizerWrapper .. py:attribute:: relcat_config :type: medcat.config.config_rel_cat.ConfigRelCAT .. py:attribute:: model_config :type: medcat.components.addons.relation_extraction.config.RelExtrBaseConfig :value: None .. py:attribute:: optimizer :type: torch.optim.AdamW :value: None .. py:attribute:: scheduler :type: torch.optim.lr_scheduler.MultiStepLR :value: None .. py:attribute:: task :type: str :value: 'train' .. py:attribute:: epoch :type: int :value: 0 .. py:attribute:: best_f1 :type: float :value: 0.0 .. py:attribute:: pad_id .. py:attribute:: padding_seq .. py:method:: save(save_path) Saves model and its dependencies to specified save_path folder. The CDB is obviously not saved, it is however necessary to save the tokenizer used. :param save_path: folder path in which to save the model & deps. :type save_path: str .. py:method:: load(pretrained_model_name_or_path = './') :classmethod: :param pretrained_model_name_or_path: Path to RelCAT model. Defaults to "./". :type pretrained_model_name_or_path: str :Returns: **RelExtrBaseComponent** -- component. .. py:method:: from_relcat_config(relcat_config, pretrained_model_name_or_path = './') :classmethod: