medcat.components.addons.relation_extraction.base_component

Attributes

logger

Classes

`ConfigRelCAT`	The RelCAT part of the config
`RelExtrBaseModel`	Base class for the RelCAT models
`Pad_Sequence`
`BaseTokenizerWrapper`	Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
`RelExtrBaseConfig`	Base class for the RelCAT models
`RelExtrBaseComponent`

Functions

`serialise`(serialiser_type, obj, target_folder[, overwrite])	Serialise an object based on the specified serialiser type.
`load_state`(model, optimizer, scheduler[, path, ...])	Used by RelCAT.load() and RelCAT.train()
`save_state`(model, optimizer, scheduler[, epoch, ...])	Used by RelCAT.save() and RelCAT.train()

Module Contents

class medcat.components.addons.relation_extraction.base_component.ConfigRelCAT(/, **data)

Bases: medcat.config.config.ComponentConfig

The RelCAT part of the config

Parameters:: data (Any)

general: General

model: Model

train: Train

class Config

extra = 'allow'

validate_assignment = True

classmethod load(load_path='./')

Load the config from a file.

Parameters:: load_path (str) – Path to RelCAT config. Defaults to “./”.
Returns:: ConfigRelCAT – The loaded config.
Return type:: ConfigRelCAT

comp_name: str = 'default'

The name of the component.

If a custom implementation is required, it needs to be registered using `medcat.components.types.register_core_component(

<core component type>, <component name>, <implementing class>)

By default, only the ‘default’ component is registered.

_is_dirty: bool = False

__setattr__(name, value)

Parameters:

name (str)
value (Any)

property is_dirty: bool

Return type:: bool

mark_clean()

get_strategy()

Return type:: medcat.storage.serialisables.SerialisingStrategy

classmethod get_init_attrs()

Return type:: list[str]

classmethod ignore_attrs()

Return type:: list[str]

classmethod include_properties()

Return type:: list[str]

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.

Parameters:: other (dict) – The model dump
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).

model_config: ClassVar[pydantic.config.ConfigDict]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: 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.

model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

__class_vars__: ClassVar[set[str]]: The names of the class variables defined on the model.

__private_attributes__: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]]: Metadata about the private attributes of the model.

__signature__: ClassVar[inspect.Signature]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: ClassVar[bool] = False: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[pydantic_core.CoreSchema]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[pydantic._internal._decorators.DecoratorInfos]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: 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.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']]: The name of the post-init method for the model, if defined.

__pydantic_root_model__: ClassVar[bool] = False: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[pydantic_core.SchemaSerializer]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator]: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: dict[str, Any] | None: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__: set[str]: The names of fields explicitly set during instantiation.

__pydantic_private__: dict[str, Any] | None: Values of private attributes set on the model instance.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__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.

Parameters:: data (Any)
Return type:: None

property model_extra: 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”`.
Return type:: dict[str, Any] | None

property model_fields_set: 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.
Return type:: set[str]

classmethod model_construct(_fields_set=None, **values)

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.

Parameters:

_fields_set (set[str] | None) – 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.
values (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the `Model` class with validated data.

Return type:

typing_extensions.Self

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Returns:

New model instance.

Return type:

typing_extensions.Self

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.

Parameters:

mode (Literal['json', 'python'] | str) – 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.
include (IncEx | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

Return type:

dict[str, Any]

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.

Parameters:

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

Return type:

str

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

Return type:

dict[str, Any]

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], Ellipsis]) – 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.
Return type:: str

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.

Parameters:: __context (Any)
Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

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.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – 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`.

Return type:

bool | None

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

Return type:

typing_extensions.Self

classmethod model_validate_json(json_data, *, strict=None, context=None)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – 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.

Return type:

typing_extensions.Self

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Return type:

typing_extensions.Self

classmethod __get_pydantic_core_schema__(source, handler, /)

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (pydantic.annotated_handlers.GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Returns:

A `pydantic-core` `CoreSchema`.

Return type:

pydantic_core.CoreSchema

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

core_schema (pydantic_core.CoreSchema) – 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.
handler (pydantic.annotated_handlers.GetJsonSchemaHandler) – 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.

Return type:

pydantic.json_schema.JsonSchemaValue

classmethod __pydantic_init_subclass__(**kwargs)

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.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

classmethod __class_getitem__(typevar_values)

Parameters:: typevar_values (type[Any] | tuple[type[Any], Ellipsis])
Return type:: type[BaseModel] | pydantic._internal._forward_ref.PydanticRecursiveRef

__copy__()

Returns a shallow copy of the model.

Return type:: typing_extensions.Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Parameters:: memo (dict[int, Any] | None)
Return type:: typing_extensions.Self

__getattr__(item)

Parameters:: item (str)
Return type:: Any

_check_frozen(name, value)

Parameters:

name (str)
value (Any)

Return type:

None

__getstate__()

Return type:: dict[Any, Any]

__setstate__(state)

Parameters:: state (dict[Any, Any])
Return type:: None

__eq__(other)

Parameters:: other (Any)
Return type:: bool

classmethod __init_subclass__(**kwargs)

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.)

Parameters:: **kwargs (typing_extensions.Unpack[pydantic.config.ConfigDict]) – 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.

__iter__()

So dict(model) works.

Return type:: TupleGenerator

__repr__()

Return type:: str

__repr_args__()

Return type:: pydantic._internal._repr.ReprArgs

__repr_name__

__repr_str__

__pretty__

__rich_repr__

__str__()

Return type:: str

property __fields__: dict[str, pydantic.fields.FieldInfo]

Return type:: dict[str, pydantic.fields.FieldInfo]

property __fields_set__: set[str]

Return type:: set[str]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Parameters:

include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)

Return type:

Dict[str, Any]

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)

Parameters:

include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)

Return type:

str

classmethod parse_obj(obj)

Parameters:: obj (Any)
Return type:: typing_extensions.Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Parameters:

b (str | bytes)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)

Return type:

typing_extensions.Self

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Parameters:

path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)

Return type:

typing_extensions.Self

classmethod from_orm(obj)

Parameters:: obj (Any)
Return type:: typing_extensions.Self

classmethod construct(_fields_set=None, **values)

Parameters:

_fields_set (set[str] | None)
values (Any)

Return type:

typing_extensions.Self

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) `

Parameters:

include (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – 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.

Return type:

typing_extensions.Self

classmethod schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE)

Parameters:

by_alias (bool)
ref_template (str)

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, **dumps_kwargs)

Parameters:

by_alias (bool)
ref_template (str)
dumps_kwargs (Any)

Return type:

str

classmethod validate(value)

Parameters:: value (Any)
Return type:: typing_extensions.Self

classmethod update_forward_refs(**localns)

Parameters:: localns (Any)
Return type:: None

_iter(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

_calculate_keys(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

medcat.components.addons.relation_extraction.base_component.serialise(serialiser_type, obj, target_folder, overwrite=False)

Serialise an object based on the specified serialiser type.

Parameters:

serialiser_type (Union[str, AvailableSerialisers]) – The serialiser type.
obj (Serialisable) – The object to serialise.
target_folder (str) – The folder to serialise into.
overwrite (bool) – Whether to allow overwriting. Defaults to False.

Return type:

None

class medcat.components.addons.relation_extraction.base_component.RelExtrBaseModel(relcat_config, model_config, pretrained_model_name_or_path)

Bases: BaseModelBluePrint

Base class for the RelCAT models

Parameters:

relcat_config (medcat.config.config_rel_cat.ConfigRelCAT)
model_config (medcat.components.addons.relation_extraction.config.RelExtrBaseConfig)

name = 'basemodel_relcat'

__init__(relcat_config, model_config, pretrained_model_name_or_path)

Class to hold the HF model + model_config

Parameters:

pretrained_model_name_or_path (str) – 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.
relcat_config (ConfigRelCAT) – relcat config.
model_config (PretrainedConfig) – HF bert config for model.

relcat_config: medcat.config.config_rel_cat.ConfigRelCAT

model_config: medcat.components.addons.relation_extraction.config.RelExtrBaseConfig

hf_model

pretrained_model_name_or_path: str

_reinitialize_dense_and_frozen_layers(relcat_config)

Reinitialize the dense layers of the model

Parameters:: relcat_config (ConfigRelCAT) – relcat config.
Return type:: None

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

Parameters:

input_ids (torch.Tensor) – input token ids. Defaults to None.
attention_mask (torch.Tensor) – attention mask for the input ids. Defaults to None.
token_type_ids (torch.Tensor) – token type ids for the input ids. Defaults to None.
position_ids (Any) – The position IDs. Defaults to None.
head_mask (Any) – The head mask. Defaults to None.
encoder_hidden_states (Any) – Encoder hidden states. Defaults to None.
encoder_attention_mask (Any) – Encoder attention mask. Defaults to None.
Q (Any) –
1. Defaults to None.
e1_e2_start (Any) – Start and end indices for the entities in the input ids. Defaults to None.
pooled_output (Any) – The pooled output. Defaults to None.

Returns:

Optional[tuple[torch.Tensor, torch.Tensor]] – Logits for the relation classification task.

Return type:

tuple[torch.Tensor, torch.Tensor]

output2logits(pooled_output, sequence_output, input_ids, e1_e2_start)

Parameters:

pooled_output (torch.Tensor) – embedding of the CLS token
sequence_output (torch.Tensor) – hidden states/embeddings for each token in the input text
input_ids (torch.Tensor) – input token ids.
e1_e2_start (torch.Tensor) – annotation tags token position

Returns:

torch.Tensor – classification probabilities for each token.

Return type:

torch.Tensor

classmethod load(pretrained_model_name_or_path, relcat_config, model_config)

Load the model from the given path

Parameters:

pretrained_model_name_or_path (str) – path to load the model from.
relcat_config (ConfigRelCAT) – relcat config.
model_config (RelExtrBaseConfig) – The model-specific config.

Returns:

RelExtrBaseModel – The loaded model.

Return type:

RelExtrBaseModel

drop_out: torch.nn.Dropout

fc1: torch.nn.Linear

fc2: torch.nn.Linear

fc3: torch.nn.Linear

dump_patches: bool = False

_version: int = 1

This allows better BC support for load_state_dict(). In 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.

training: bool

_parameters: Dict[str, torch.nn.parameter.Parameter | None]

_buffers: Dict[str, torch.Tensor | None]

_non_persistent_buffers_set: Set[str]

_backward_pre_hooks: Dict[int, Callable]

_backward_hooks: Dict[int, Callable]

_is_full_backward_hook: bool | None

_forward_hooks: Dict[int, Callable]

_forward_hooks_with_kwargs: Dict[int, bool]

_forward_hooks_always_called: Dict[int, bool]

_forward_pre_hooks: Dict[int, Callable]

_forward_pre_hooks_with_kwargs: Dict[int, bool]

_state_dict_hooks: Dict[int, Callable]

_load_state_dict_pre_hooks: Dict[int, Callable]

_state_dict_pre_hooks: Dict[int, Callable]

_load_state_dict_post_hooks: Dict[int, Callable]

_modules: Dict[str, Module | None]

call_super_init: bool = False

_compiled_call_impl: Callable | None = None

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 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 state_dict.

Buffers can be accessed as attributes using given names.

Parameters:

name (str) – name of the buffer. The buffer can be accessed from this module using the given name
tensor (Tensor or None) – buffer to be registered. If None, then operations that run on buffers, such as cuda, are ignored. If None, the buffer is not included in the module’s state_dict.
persistent (bool) – whether the buffer is part of this module’s state_dict.

Return type:

None

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> self.register_buffer('running_mean', torch.zeros(num_features))

register_parameter(name, param)

Add a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters:

name (str) – name of the parameter. The parameter can be accessed from this module using the given name
param (Parameter or None) – parameter to be added to the module. If None, then operations that run on parameters, such as cuda, are ignored. If None, the parameter is not included in the module’s state_dict.

Return type:

None

add_module(name, module)

Add a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters:

name (str) – name of the child module. The child module can be accessed from this module using the given name
module (Module) – child module to be added to the module.

Return type:

None

register_module(name, module)

Alias for add_module().

Parameters:

name (str)
module (Optional[Module])

Return type:

None

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:

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.

Parameters:: target (str) – 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
Return type:: Module

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:

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)).

Parameters:

target (str) – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
module (Module) – The module to set the submodule to.

Raises:

ValueError – If the target string is empty
AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Module

Return type:

None

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.

Parameters:: target (str) – 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
Return type:: torch.nn.parameter.Parameter

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.

Parameters:: target (str) – 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
Return type:: torch.Tensor

get_extra_state()

Return any extra state to include in the module’s state_dict.

Implement this and a corresponding 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
Return type:: Any

set_extra_state(state)

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict() to handle any extra state found within the state_dict. Implement this function and a corresponding get_extra_state() for your module if you need to store extra state within its state_dict.

Parameters:: state (dict) – Extra state from the state_dict
Return type:: None

_apply(fn, recurse=True)

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 nn-init-doc).

Parameters:: fn (Module -> None) – function to be applied to each submodule
Returns:: Module – self
Return type:: T

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)
)

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.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: Module – self
Return type:: T

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.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: Module – self
Return type:: T

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.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: Module – self
Return type:: T

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.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: Module – self
Return type:: T

cpu()

Move all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns:: Module – self
Return type:: T

type(dst_type)

Casts all parameters and buffers to dst_type.

Note

This method modifies the module in-place.

Parameters:: dst_type (type or string) – the desired type
Returns:: Module – self
Return type:: T

float()

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns:: Module – self
Return type:: T

double()

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns:: Module – self
Return type:: T

half()

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns:: Module – self
Return type:: T

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns:: Module – self
Return type:: T

to_empty(*, device, recurse=True)

Move the parameters and buffers to the specified device without copying storage.

Parameters:

device (torch.device) – The desired device of the parameters and buffers in this module.
recurse (bool) – Whether parameters and buffers of submodules should be recursively moved to the specified device.

Returns:

Module – self

Return type:

T

to(device: torch._prims_common.DeviceLikeType | None = ..., dtype: Module.to.dtype | None = ..., 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

to(device=None, dtype=None, non_blocking=False)

to(dtype, non_blocking=False)

to(tensor, non_blocking=False)

to(memory_format=torch.channels_last)

Its signature is similar to torch.Tensor.to(), but only accepts floating point or complex dtypes. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When 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.

Parameters:

device (torch.device) – the desired device of the parameters and buffers in this module
dtype (torch.dtype) – the desired floating point or complex dtype of the parameters and buffers in this module
tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)

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)

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 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 grad_output in subsequent computations. Entries in 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.

Parameters:

hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing backward_pre hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this torch.nn.modules.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook() will fire before all hooks registered by this method.

Returns:

:class:`torch.utils.hooks.RemovableHandle` – a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_backward_hook(hook)

Register a backward hook on the module.

This function is deprecated in favor of 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()
Parameters:: hook (Callable[[Module, _grad_t, _grad_t], Union[None, _grad_t]])
Return type:: torch.utils.hooks.RemovableHandle

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 grad_input and 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 grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and 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.

Parameters:

hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing backward hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this torch.nn.modules.Module. Note that global backward hooks registered with register_module_full_backward_hook() will fire before all hooks registered by this method.

Returns:

:class:`torch.utils.hooks.RemovableHandle` – a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

_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.

_get_backward_pre_hooks()

_maybe_warn_non_full_backward_hook(inputs, result, grad_fn)

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 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

Parameters:

hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing forward_pre hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this torch.nn.modules.Module. Note that global forward_pre hooks registered with register_module_forward_pre_hook() will fire before all hooks registered by this method. Default: False
with_kwargs (bool) – If true, the hook will be passed the kwargs given to the forward function. Default: False

Returns:

:class:`torch.utils.hooks.RemovableHandle` – a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

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 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 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

Parameters:

hook (Callable) – The user defined hook to be registered.
prepend (bool) – If True, the provided hook will be fired before all existing forward hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this torch.nn.modules.Module. Note that global forward hooks registered with register_module_forward_hook() will fire before all hooks registered by this method. Default: False
with_kwargs (bool) – If True, the hook will be passed the kwargs given to the forward function. Default: False
always_call (bool) – If True the hook will be run regardless of whether an exception is raised while calling the Module. Default: False

Returns:

:class:`torch.utils.hooks.RemovableHandle` – a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

_slow_forward(*input, **kwargs)

_wrapped_call_impl(*args, **kwargs)

_call_impl(*args, **kwargs)

__call__: Callable[Ellipsis, Any]

__getstate__()

__setstate__(state)

__getattr__(name)

Parameters:: name (str)
Return type:: Any

__setattr__(name, value)

Parameters:

name (str)
value (Union[torch.Tensor, Module])

Return type:

None

__delattr__(name)

_register_state_dict_hook(hook)

Register a post-hook for the 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 state_dict() is called from.

register_state_dict_post_hook(hook)

Register a post-hook for the 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.

register_state_dict_pre_hook(hook)

Register a pre-hook for the 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.

_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 state_dict().

In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic.

Parameters:

destination (dict) – a dict where state will be stored
prefix (str) – the prefix for parameters and buffers used in this module

T_destination

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.

Parameters:

destination (dict, optional) – 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.
prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.
keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

dict – a dictionary containing a whole state of the module

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

_register_load_state_dict_pre_hook(hook, with_module=False)

See 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 register_load_state_dict_pre_hook() always takes the module as the first argument.

Parameters:

hook (Callable) – Callable hook that will be invoked before loading the state dict.
with_module (bool, optional) – Whether or not to pass the module instance to the hook as the first parameter.

register_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before module’s 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

Parameters:: hook (Callable) – Callable hook that will be invoked before loading the state dict.

register_load_state_dict_post_hook(hook)

Register a post-hook to be run after module’s 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 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()

_load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)

Copy parameters and buffers from state_dict into only this module, but not its descendants.

This is called on every submodule in load_state_dict(). Metadata saved for this module in input state_dict is provided as local_metadata. For state dicts without metadata, local_metadata is empty. Subclasses can achieve class-specific backward compatible loading using the version number at local_metadata.get(“version”, None). Additionally, 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

state_dict is not the same object as the input state_dict to load_state_dict(). So it can be modified.

Parameters:

state_dict (dict) – a dict containing parameters and persistent buffers.
prefix (str) – the prefix for parameters and buffers used in this module
local_metadata (dict) – a dict containing the metadata for this module. See
strict (bool) – whether to strictly enforce that the keys in state_dict with prefix match the names of parameters and buffers in this module
missing_keys (list of str) – if strict=True, add missing keys to this list
unexpected_keys (list of str) – if strict=True, add unexpected keys to this list
error_msgs (list of str) – error messages should be added to this list, and will be reported together in load_state_dict()

load_state_dict(state_dict, strict=True, assign=False)

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Warning

If assign is True the optimizer must be created after the call to load_state_dict unless get_swap_module_params_on_conversion() is True.

Parameters:

state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True
assign (bool, optional) – 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 Default: ``False`

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 state_dict, load_state_dict() will raise a RuntimeError.

_named_members(get_members_fn, prefix='', recurse=True, remove_duplicate=True)

Help yield various names + members of modules.

Parameters:: remove_duplicate (bool)

parameters(recurse=True)

Return an iterator over module parameters.

This is typically passed to an optimizer.

Parameters:: recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields:: Parameter – module parameter
Return type:: Iterator[torch.nn.parameter.Parameter]

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for param in model.parameters():
>>>     print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

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.

Parameters:

prefix (str) – prefix to prepend to all parameter names.
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Return type:

Iterator[Tuple[str, torch.nn.parameter.Parameter]]

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

buffers(recurse=True)

Return an iterator over module buffers.

Parameters:: recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:: torch.Tensor – module buffer
Return type:: Iterator[torch.Tensor]

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

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.

Parameters:

prefix (str) – prefix to prepend to all buffer names.
recurse (bool, optional) – 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.
remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Return type:

Iterator[Tuple[str, torch.Tensor]]

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

children()

Return an iterator over immediate children modules.

Yields:: Module – a child module
Return type:: Iterator[Module]

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
Return type:: Iterator[Tuple[str, Module]]

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)

modules()

Return an iterator over all modules in the network.

Yields:: Module – a module in the network
Return type:: Iterator[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.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)

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.

Parameters:

memo (Optional[Set[Module]]) – a memo to store the set of modules already added to the result
prefix (str) – a prefix that will be added to the name of the module
remove_duplicate (bool) – 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))

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. Dropout, BatchNorm, etc.

Parameters:: mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns:: Module – self
Return type:: T

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. Dropout, BatchNorm, etc.

This is equivalent with self.train(False).

See locally-disable-grad-doc for a comparison between .eval() and several similar mechanisms that may be confused with it.

Returns:: Module – self
Return type:: T

requires_grad_(requires_grad=True)

Change if autograd should record operations on parameters in this module.

This method sets the parameters’ 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 locally-disable-grad-doc for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.

Parameters:: requires_grad (bool) – whether autograd should record operations on parameters in this module. Default: True.
Returns:: Module – self
Return type:: T

zero_grad(set_to_none=True)

Reset gradients of all model parameters.

See similar function under torch.optim.Optimizer for more context.

Parameters:: set_to_none (bool) – instead of setting to zero, set the grads to None. See torch.optim.Optimizer.zero_grad() for details.
Return type:: None

share_memory()

See torch.Tensor.share_memory_().

Return type:: T

_get_name()

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.

Return type:: str

__repr__()

__dir__()

_replicate_for_data_parallel()

compile(*args, **kwargs)

Compile this Module’s forward using torch.compile().

This Module’s __call__ method is compiled and all arguments are passed as-is to torch.compile().

See torch.compile() for details on the arguments for this function.

class medcat.components.addons.relation_extraction.base_component.Pad_Sequence(seq_pad_value, label_pad_value=-1)

Parameters:

seq_pad_value (int)
label_pad_value (int)

__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.

Parameters:

seq_pad_value (int) – pad value for input_ids.
label_pad_value (int) – pad value for labels. Defaults to -1.

seq_pad_value: int

label_pad_value: int = -1

__call__(batch)

Pads a batch of input_ids.

Parameters:: batch (list[torch.Tensor]) – 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.
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
Return type:: tuple[torch.Tensor, list, torch.Tensor, torch.LongTensor, torch.LongTensor]

class medcat.components.addons.relation_extraction.base_component.BaseTokenizerWrapper(hf_tokenizers=None, max_seq_length=None, add_special_tokens=False)

Bases: 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…).

Parameters:

max_seq_length (Optional[int])
add_special_tokens (Optional[bool])

name = 'base_tokenizer_wrapper_rel'

__init__(hf_tokenizers=None, max_seq_length=None, add_special_tokens=False)

Parameters:

max_seq_length (Optional[int])
add_special_tokens (Optional[bool])

hf_tokenizers = None

max_seq_length = None

_add_special_tokens = False

get_size()

token_to_id(token)

get_pad_id()

__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.

Parameters:

text (str, List[str], List[List[str]], optional) – 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).
text_pair (str, List[str], List[List[str]], optional) – 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).
text_target (str, List[str], List[List[str]], optional) – 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).
text_pair_target (str, List[str], List[List[str]], optional) – 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).
truncation (Optional[bool])

save(dir_path)

Parameters:: dir_path (str)

classmethod load(tokenizer_path, relcat_config, **kwargs)

Parameters:

tokenizer_path (str)
relcat_config (medcat.config.config_rel_cat.ConfigRelCAT)

Return type:

BaseTokenizerWrapper

_backends = ['tokenizers']

vocab_files_names

slow_tokenizer_class: transformers.tokenization_utils.PreTrainedTokenizer = None

add_prefix_space

_tokenizer

_decode_use_source_tokenizer = False

property is_fast: bool

Return type:: bool

property can_save_slow_tokenizer: 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
Return type:: bool

property vocab_size: int

Size of the base vocabulary (without the added tokens).

Type:: int
Return type:: int

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.
Return type:: Dict[str, int]

property vocab: Dict[str, int]

Return type:: Dict[str, int]

property added_tokens_encoder: 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.

Return type:: Dict[str, int]

property added_tokens_decoder: 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.
Return type:: Dict[int, transformers.tokenization_utils_base.AddedToken]

get_added_vocab()

Returns the added tokens in the vocabulary as a dictionary of token to index.

Returns:: `Dict[str, int]` – The added tokens.
Return type:: Dict[str, int]

__len__()

Size of the full vocabulary with the added tokens.

Return type:: int

property backend_tokenizer: tokenizers.Tokenizer

The Rust tokenizer used as a backend.

Type:: tokenizers.implementations.BaseTokenizer
Return type:: tokenizers.Tokenizer

property decoder: tokenizers.decoders.Decoder

The Rust decoder for this tokenizer.

Type:: tokenizers.decoders.Decoder
Return type:: tokenizers.decoders.Decoder

_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)

Parameters:

encoding (tokenizers.Encoding)
return_token_type_ids (Optional[bool])
return_attention_mask (Optional[bool])
return_overflowing_tokens (bool)
return_special_tokens_mask (bool)
return_offsets_mapping (bool)
return_length (bool)
verbose (bool)

Return type:

Tuple[Dict[str, Any], List[tokenizers.Encoding]]

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.

Parameters:: tokens (str or Iterable[str]) – One or several token(s) to convert to token id(s).
Returns:: `int` or `List[int]` – The token id or list of token ids.
Return type:: Union[int, List[int]]

_convert_token_to_id_with_added_voc(token)

Parameters:: token (str)
Return type:: int

_convert_id_to_token(index)

Parameters:: index (int)
Return type:: Optional[str]

_add_tokens(new_tokens, special_tokens=False)

Parameters:: new_tokens (List[Union[str, transformers.tokenization_utils_base.AddedToken]])
Return type:: int

num_special_tokens_to_add(pair=False)

Returns the number of added tokens when encoding a sequence with special tokens.

<Tip>

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.

</Tip>

Parameters:: pair (bool, optional, defaults to False) – Whether the number of added tokens should be computed in the case of a sequence pair or a single sequence.
Returns:: `int` – Number of special tokens added to sequences.
Return type:: int

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.

Parameters:

ids (int or List[int]) – The token id (or token ids) to convert to tokens.
skip_special_tokens (bool, optional, defaults to False) – Whether or not to remove special tokens in the decoding.

Returns:

`str` or `List[str]` – The decoded token(s).

Return type:

Union[str, List[str]]

tokenize(text, pair=None, add_special_tokens=False, **kwargs)

Converts a string into a sequence of tokens, replacing unknown tokens with the unk_token.

Parameters:

text (str) – The sequence to be encoded.
pair (str, optional) – A second sequence to be encoded with the first.
add_special_tokens (bool, optional, defaults to False) – Whether or not to add the special tokens associated with the corresponding model.
kwargs (additional keyword arguments, optional) – Will be passed to the underlying model specific encode method. See details in [~PreTrainedTokenizerBase.__call__]

Returns:

`List[str]` – The list of tokens.

Return type:

List[str]

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.

Parameters:

padding_strategy ([~utils.PaddingStrategy]) – The kind of padding that will be applied to the input
truncation_strategy ([~tokenization_utils_base.TruncationStrategy]) – The kind of truncation that will be applied to the input
max_length (int) – The maximum size of a sequence.
stride (int) – The stride to use when handling overflow.
pad_to_multiple_of (int, optional) – 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).
padding_side (str, optional) – 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.

_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)

Parameters:

batch_text_or_text_pairs (Union[List[transformers.tokenization_utils_base.TextInput], List[transformers.tokenization_utils_base.TextInputPair], List[transformers.tokenization_utils_base.PreTokenizedInput], List[transformers.tokenization_utils_base.PreTokenizedInputPair]])
add_special_tokens (bool)
padding_strategy (transformers.utils.PaddingStrategy)
truncation_strategy (transformers.tokenization_utils_base.TruncationStrategy)
max_length (Optional[int])
stride (int)
is_split_into_words (bool)
pad_to_multiple_of (Optional[int])
padding_side (Optional[bool])
return_tensors (Optional[str])
return_token_type_ids (Optional[bool])
return_attention_mask (Optional[bool])
return_overflowing_tokens (bool)
return_special_tokens_mask (bool)
return_offsets_mapping (bool)
return_length (bool)
verbose (bool)
split_special_tokens (bool)

Return type:

transformers.tokenization_utils_base.BatchEncoding

_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)

Parameters:

text (Union[transformers.tokenization_utils_base.TextInput, transformers.tokenization_utils_base.PreTokenizedInput])
text_pair (Optional[Union[transformers.tokenization_utils_base.TextInput, transformers.tokenization_utils_base.PreTokenizedInput]])
add_special_tokens (bool)
padding_strategy (transformers.utils.PaddingStrategy)
truncation_strategy (transformers.tokenization_utils_base.TruncationStrategy)
max_length (Optional[int])
stride (int)
is_split_into_words (bool)
pad_to_multiple_of (Optional[int])
padding_side (Optional[bool])
return_tensors (Optional[bool])
return_token_type_ids (Optional[bool])
return_attention_mask (Optional[bool])
return_overflowing_tokens (bool)
return_special_tokens_mask (bool)
return_offsets_mapping (bool)
return_length (bool)
verbose (bool)
split_special_tokens (bool)

Return type:

transformers.tokenization_utils_base.BatchEncoding

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.

Parameters:: tokens (List[str]) – The token to join in a string.
Returns:: `str` – The joined tokens.
Return type:: str

_decode(token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=None, **kwargs)

Parameters:

token_ids (Union[int, List[int]])
skip_special_tokens (bool)
clean_up_tokenization_spaces (bool)

Return type:

str

_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}.

Parameters:

save_directory (Union[str, os.PathLike])
file_names (Tuple[str])
legacy_format (Optional[bool])
filename_prefix (Optional[str])

Return type:

Tuple[str]

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.

Parameters:

text_iterator (generator of List[str]) – 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.
vocab_size (int) – The size of the vocabulary you want for your tokenizer.
length (int, optional) – The total number of sequences in the iterator. This is used to provide meaningful progress tracking
new_special_tokens (list of str or AddedToken, optional) – A list of new special tokens to add to the tokenizer you are training.
special_tokens_map (Dict[str, str], optional) – 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.
kwargs (Dict[str, Any], optional) – Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library.

Returns:

[`PreTrainedTokenizerFast`] – A new tokenizer of the same type as the original one, trained on
`text_iterator`.

pretrained_vocab_files_map: Dict[str, Dict[str, str]]

_auto_class: str | None = None

model_input_names: List[str] = ['input_ids', 'token_type_ids', 'attention_mask']

padding_side: str = 'right'

truncation_side: str = 'right'

init_inputs = ()

init_kwargs

name_or_path

_processor_class

model_max_length

clean_up_tokenization_spaces

split_special_tokens

deprecation_warnings

_in_target_context_manager = False

chat_template

extra_special_tokens

property max_len_single_sentence: int

The maximum length of a sentence that can be fed to the model.

Type:: int
Return type:: int

property max_len_sentences_pair: int

The maximum combined length of a pair of sentences that can be fed to the model.

Type:: int
Return type:: int

_set_processor_class(processor_class)

Sets processor class as an attribute.

Parameters:: processor_class (str)

__repr__()

Return type:: str

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.

Parameters:

conversation (Union[List[Dict[str, str]], List[List[Dict[str, str]]]]) – A list of dicts with “role” and “content” keys, representing the chat history so far.
tools (List[Dict], optional) – 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.
documents (List[Dict[str, str]], optional) – 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.
chat_template (str, optional) – 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.
add_generation_prompt (bool, optional) – 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.
continue_final_message (bool, optional) – 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.
tokenize (bool, defaults to True) – Whether to tokenize the output. If False, the output will be a string.
padding (bool, defaults to False) – Whether to pad sequences to the maximum length. Has no effect if tokenize is False.
truncation (bool, defaults to False) – Whether to truncate sequences at the maximum length. Has no effect if tokenize is False.
max_length (int, optional) – 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.
return_tensors (str or [~utils.TensorType], optional) – 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.
return_dict (bool, defaults to False) – Whether to return a dictionary with named outputs. Has no effect if tokenize is False.
(`Dict[str (tokenizer_kwargs) –
Any]`, optional): Additional kwargs to pass to the tokenizer.
return_assistant_tokens_mask (bool, defaults to False) – 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.
**kwargs – Additional kwargs to pass to the template renderer. Will be accessible by the chat template.
tokenizer_kwargs (Optional[Dict[str, Any]])

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.

Return type:

Union[str, List[int], List[str], List[List[int]], BatchEncoding]

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.

Parameters:

chat_template (str, optional) – 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.
tools (List[Dict], optional) – 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.

Returns:

`str` – The chat template string.

Return type:

str

classmethod 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)

Instantiate a [~tokenization_utils_base.PreTrainedTokenizerBase] (or a derived class) from a predefined tokenizer.

Parameters:

pretrained_model_name_or_path (str or os.PathLike) –
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.
cache_dir (str or os.PathLike, optional) – Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
force_download (bool, optional, defaults to False) – Whether or not to force the (re-)download the vocabulary files and override the cached versions if they exist.
resume_download – Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
proxies (Dict[str, str], optional) – 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.
token (str or bool, optional) – 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).
local_files_only (bool, optional, defaults to False) – Whether or not to only rely on local files and not to attempt to download any files.
revision (str, optional, defaults to “main”) – 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.
subfolder (str, optional) – 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.
inputs (additional positional arguments, optional) – Will be passed along to the Tokenizer __init__ method.
trust_remote_code (bool, optional, defaults to False) – 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.
kwargs (additional keyword arguments, optional) – 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.

<Tip>

Passing token=True is required when you want to use a private model.

</Tip>

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=”<unk>”) # You should be sure ‘<unk>’ is in the vocabulary when doing that. # Otherwise use tokenizer.add_special_tokens({‘unk_token’: ‘<unk>’}) instead) assert tokenizer.unk_token == “<unk>” ```

classmethod _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)

static _eventually_correct_t5_max_length(pretrained_model_name_or_path, max_model_length, init_max_model_length)

classmethod convert_added_tokens(obj, save=False, add_type_field=True)

Parameters:: obj (Union[tokenizers.AddedToken, Any])

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).

Parameters:

save_directory (str or os.PathLike) – The path to a directory where the tokenizer will be saved.
legacy_format (bool, optional) –
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.
filename_prefix (str, optional) – A prefix to add to the names of the files saved by the tokenizer.
push_to_hub (bool, optional, defaults to False) – 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).
kwargs (Dict[str, Any], optional) – Additional key word arguments passed along to the [~utils.PushToHubMixin.push_to_hub] method.

Returns:

A tuple of `str` – The files saved.

Return type:

Tuple[str]

abstract save_vocabulary(save_directory, filename_prefix=None)

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.

Parameters:

save_directory (str) – The directory in which to save the vocabulary.
filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns:

`Tuple (str) – Paths to the files saved.

Return type:

Tuple[str]

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)).

Parameters:

text (str, List[str] or List[int]) – 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).
text_pair (str, List[str] or List[int], optional) – 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).
add_special_tokens (bool)
padding (Union[bool, str, transformers.utils.PaddingStrategy])
truncation (Union[bool, str, TruncationStrategy])
max_length (Optional[int])
stride (int)
padding_side (Optional[bool])
return_tensors (Optional[Union[str, transformers.utils.TensorType]])

Return type:

List[int]

_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.

_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)

Parameters:

text (Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]])
text_pair (Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]])
add_special_tokens (bool)
padding (Union[bool, str, transformers.utils.PaddingStrategy])
truncation (Union[bool, str, TruncationStrategy])
max_length (Optional[int])
stride (int)
is_split_into_words (bool)
pad_to_multiple_of (Optional[int])
padding_side (Optional[bool])
return_tensors (Optional[Union[str, transformers.utils.TensorType]])
return_token_type_ids (Optional[bool])
return_attention_mask (Optional[bool])
return_overflowing_tokens (bool)
return_special_tokens_mask (bool)
return_offsets_mapping (bool)
return_length (bool)
verbose (bool)
split_special_tokens (bool)

Return type:

BatchEncoding

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.

</Tip>

Parameters:

text (str, List[str] or (for non-fast tokenizers) List[int]) – 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).
text_pair (str, List[str] or List[int], optional) – 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).
add_special_tokens (bool)
padding (Union[bool, str, transformers.utils.PaddingStrategy])
truncation (Union[bool, str, TruncationStrategy])
max_length (Optional[int])
stride (int)
is_split_into_words (bool)
pad_to_multiple_of (Optional[int])
padding_side (Optional[bool])
return_tensors (Optional[Union[str, transformers.utils.TensorType]])
return_token_type_ids (Optional[bool])
return_attention_mask (Optional[bool])
return_overflowing_tokens (bool)
return_special_tokens_mask (bool)
return_offsets_mapping (bool)
return_length (bool)
verbose (bool)

Return type:

BatchEncoding

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.

</Tip>

Parameters:

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]]]) – 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).
add_special_tokens (bool)
padding (Union[bool, str, transformers.utils.PaddingStrategy])
truncation (Union[bool, str, TruncationStrategy])
max_length (Optional[int])
stride (int)
is_split_into_words (bool)
pad_to_multiple_of (Optional[int])
padding_side (Optional[bool])
return_tensors (Optional[Union[str, transformers.utils.TensorType]])
return_token_type_ids (Optional[bool])
return_attention_mask (Optional[bool])
return_overflowing_tokens (bool)
return_special_tokens_mask (bool)
return_offsets_mapping (bool)
return_length (bool)
verbose (bool)
split_special_tokens (bool)

Return type:

BatchEncoding

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.

<Tip>

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.

</Tip>

Parameters:

encoded_inputs ([BatchEncoding], list of [BatchEncoding], Dict[str, List[int]], Dict[str, List[List[int]] or List[Dict[str, List[int]]]) –
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.
padding (bool, str or [~utils.PaddingStrategy], optional, defaults to True) –

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).
max_length (int, optional) – Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (int, optional) –
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).
padding_side (str, optional) – 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.
return_attention_mask (bool, optional) –
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)
return_tensors (str or [~utils.TensorType], optional) –
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.
verbose (bool, optional, defaults to True) – Whether or not to print more information and warnings.

Return type:

BatchEncoding

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.

Parameters:

token_ids_0 (List[int]) – The first tokenized sequence.
token_ids_1 (List[int], optional) – The second tokenized sequence.

Returns:

`List[int]` – The token type ids.

Return type:

List[int]

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.

Parameters:

token_ids_0 (List[int]) – The first tokenized sequence.
token_ids_1 (List[int], optional) – The second tokenized sequence.

Returns:

`List[int]` – The model input with special tokens.

Return type:

List[int]

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.

Parameters:

ids (List[int]) – Tokenized input ids of the first sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
pair_ids (List[int], optional) – Tokenized input ids of the second sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
add_special_tokens (bool)
padding (Union[bool, str, transformers.utils.PaddingStrategy])
truncation (Union[bool, str, TruncationStrategy])
max_length (Optional[int])
stride (int)
pad_to_multiple_of (Optional[int])
padding_side (Optional[bool])
return_tensors (Optional[Union[str, transformers.utils.TensorType]])
return_token_type_ids (Optional[bool])
return_attention_mask (Optional[bool])
return_overflowing_tokens (bool)
return_special_tokens_mask (bool)
return_offsets_mapping (bool)
return_length (bool)
verbose (bool)
prepend_batch_axis (bool)

Return type:

BatchEncoding

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.

Parameters:

ids (List[int]) – Tokenized input ids of the first sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
pair_ids (List[int], optional) – Tokenized input ids of the second sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
num_tokens_to_remove (int, optional, defaults to 0) – Number of tokens to remove using the truncation strategy.
truncation_strategy (str or [~tokenization_utils_base.TruncationStrategy], optional, defaults to ‘longest_first’) –
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).
stride (int, optional, defaults to 0) – 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.

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)

Return type:

Tuple[List[int], List[int], List[int]]

_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)

Parameters:

encoded_inputs (Union[Dict[str, EncodedInput], BatchEncoding]) – Dictionary of tokenized inputs (List[int]) or batch of tokenized inputs (List[List[int]]).
max_length (Optional[int]) – maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens.
padding_strategy (transformers.utils.PaddingStrategy) –
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
pad_to_multiple_of (Optional[int]) – (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).
padding_side (Optional[bool]) – 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.
return_attention_mask (Optional[bool]) – (optional) Set to False to avoid returning attention mask (default: set to model specifics)

Return type:

dict

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.

Parameters:

sequences (Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]) – List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) – Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional) – Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces.
kwargs (additional keyword arguments, optional) – Will be passed to the underlying model specific decode method.

Returns:

`List[str]` – The list of decoded sentences.

Return type:

List[str]

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)).

Parameters:

token_ids (Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]) – List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) – Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional) – Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces.
kwargs (additional keyword arguments, optional) – Will be passed to the underlying model specific decode method.

Returns:

`str` – The decoded sentence.

Return type:

str

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.

Parameters:

token_ids_0 (List[int]) – List of ids of the first sequence.
token_ids_1 (List[int], optional) – List of ids of the second sequence.
already_has_special_tokens (bool, optional, defaults to False) – Whether or not the token list is already formatted with special tokens for the model.

Returns:

A list of integers in the range [0, 1] – 1 for a special token, 0 for a sequence token.

Return type:

List[int]

static clean_up_tokenization(out_string)

Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.

Parameters:: out_string (str) – The text to clean up.
Returns:: `str` – The cleaned-up string.
Return type:: str

_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

Parameters:

ids (List[str]) – The ids produced by the tokenization
max_length (int, optional) – The max_length desired (does not trigger a warning if it is set)
verbose (bool) – Whether or not to print more information and warnings.

_switch_to_input_mode(): Private method to put the tokenizer in input mode (when it has different modes for input/outputs)

_switch_to_target_mode(): Private method to put the tokenizer in target mode (when it has different modes for input/outputs)

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.

classmethod register_for_auto_class(auto_class='AutoTokenizer')

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.

</Tip>

Parameters:: auto_class (str or type, optional, defaults to “AutoTokenizer”) – The auto class to register this new tokenizer with.

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.

Parameters:

src_texts (List[str]) – List of documents to summarize or source language texts.
tgt_texts (list, optional) – List of summaries or target language texts.
max_length (int, optional) – 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.
max_target_length (int, optional) – 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.
padding (bool, str or [~utils.PaddingStrategy], optional, defaults to False) –
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).
return_tensors (str or [~utils.TensorType], optional) –
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.
truncation (bool, str or [~tokenization_utils_base.TruncationStrategy], optional, defaults to True) –
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).
**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.

Return type:

[BatchEncoding]

SPECIAL_TOKENS_ATTRIBUTES = ['bos_token', 'eos_token', 'unk_token', 'sep_token', 'pad_token', 'cls_token', 'mask_token',...

_pad_token_type_id = 0

verbose = False

_special_tokens_map

sanitize_special_tokens()

The sanitize_special_tokens is now deprecated kept for backward compatibility and will be removed in transformers v5.

Return type:: int

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 ‘</s>’).

Parameters:

special_tokens_dict (dictionary str to str or tokenizers.AddedToken) –
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).
replace_additional_special_tokens (bool, optional,, defaults to True) – 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.

Returns:

`int` – Number of tokens added to the vocabulary.

Return type:

int

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”: “<CLS>”}

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 == “<CLS>” ```

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.

Parameters:

new_tokens (str, tokenizers.AddedToken or a list of str or tokenizers.AddedToken) – 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.
special_tokens (bool, optional, defaults to False) –
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.

Returns:

`int` – Number of tokens added to the vocabulary.

Return type:

int

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)) ```

property pad_token_type_id: int

Id of the padding token type in the vocabulary.

Type:: int
Return type:: int

__setattr__(key, value)

__getattr__(key)

property special_tokens_map: Dict[str, str | List[str]]

A dictionary mapping special token class attributes (cls_token, unk_token, etc.) to their values (‘<unk>’, ‘<cls>’, etc.).

Convert potential tokens of tokenizers.AddedToken type to string.

Type:: Dict[str, Union[str, List[str]]]
Return type:: Dict[str, Union[str, List[str]]]

property special_tokens_map_extended: Dict[str, str | tokenizers.AddedToken | List[str | tokenizers.AddedToken]]

A dictionary mapping special token class attributes (cls_token, unk_token, etc.) to their values (‘<unk>’, ‘<cls>’, 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]]]]
Return type:: Dict[str, Union[str, tokenizers.AddedToken, List[Union[str, tokenizers.AddedToken]]]]

property all_special_tokens_extended: List[str | tokenizers.AddedToken]

All the special tokens (‘<unk>’, ‘<cls>’, 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]]
Return type:: List[Union[str, tokenizers.AddedToken]]

property all_special_tokens: List[str]

A list of the unique special tokens (‘<unk>’, ‘<cls>’, …, etc.).

Convert tokens of tokenizers.AddedToken type to string.

Type:: List[str]
Return type:: List[str]

property all_special_ids: List[int]

List the ids of the special tokens(‘<unk>’, ‘<cls>’, etc.) mapped to class attributes.

Type:: List[int]
Return type:: List[int]

_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.

Parameters:: special_tokens (List[str])

_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.

Parameters:

repo_id (str)
private (Optional[bool])
token (Optional[Union[bool, str]])
repo_url (Optional[str])
organization (Optional[str])

Return type:

str

_get_files_timestamps(working_dir)

Returns the list of files with their last modification timestamp.

Parameters:: working_dir (Union[str, os.PathLike])

_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.

Parameters:

working_dir (Union[str, os.PathLike])
repo_id (str)
files_timestamps (Dict[str, float])
commit_message (Optional[str])
token (Optional[Union[bool, str]])
create_pr (bool)
revision (str)
commit_description (str)

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.

Parameters:

repo_id (str) – The name of the repository you want to push your {object} to. It should contain your organization name when pushing to a given organization.
use_temp_dir (bool, optional) – 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.
commit_message (str, optional) – Message to commit while pushing. Will default to “Upload {object}”.
private (bool, optional) – 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.
token (bool or str, optional) – 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.
max_shard_size (int or str, optional, defaults to “5GB”) – 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.
create_pr (bool, optional, defaults to False) – Whether or not to create a PR with the uploaded files or directly commit.
safe_serialization (bool, optional, defaults to True) – Whether or not to convert the model weights in safetensors format for safer serialization.
revision (str, optional) – Branch to push the uploaded files to.
commit_description (str, optional) – The description of the commit that will be created
tags (List[str], optional) – List of tags to push on the Hub.

Return type:

str

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”) ```

class medcat.components.addons.relation_extraction.base_component.RelExtrBaseConfig(pretrained_model_name_or_path, **kwargs)

Bases: transformers.PretrainedConfig

Base class for the RelCAT models

name = 'base-config-relcat'

__init__(pretrained_model_name_or_path, **kwargs)

model_type = 'relcat'

pretrained_model_name_or_path

hf_model_config: transformers.PretrainedConfig

to_dict()

Serializes this instance to a Python dictionary.

Returns:: `Dict[str, Any]` – Dictionary of all the attributes that make up this configuration instance.

save(save_path)

Parameters:: save_path (str)

classmethod load(pretrained_model_name_or_path, relcat_config, **kwargs)

Parameters:

pretrained_model_name_or_path (str)
relcat_config (medcat.config.config_rel_cat.ConfigRelCAT)

Return type:

RelExtrBaseConfig

base_config_key: str = ''

sub_configs: Dict[str, PretrainedConfig]

is_composition: bool = False

attribute_map: Dict[str, str]

base_model_tp_plan: Dict[str, Any] | None = None

_auto_class: str | None = None

__setattr__(key, value)

__getattribute__(key)

return_dict

output_hidden_states

output_attentions

torchscript

torch_dtype

use_bfloat16

tf_legacy_loss

pruned_heads

tie_word_embeddings

chunk_size_feed_forward

is_encoder_decoder

is_decoder

cross_attention_hidden_size

add_cross_attention

tie_encoder_decoder

architectures

finetuning_task

id2label

label2id

tokenizer_class

prefix

bos_token_id

pad_token_id

eos_token_id

sep_token_id

decoder_start_token_id

task_specific_params

problem_type

_name_or_path = ''

_commit_hash

_attn_implementation_internal

_attn_implementation_autoset = False

transformers_version

property name_or_path: str

Return type:: str

property use_return_dict: bool

Whether or not return [~utils.ModelOutput] instead of tuples.

Type:: bool
Return type:: bool

property num_labels: int

The number of labels for classification models.

Type:: int
Return type:: int

property _attn_implementation

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.

Parameters:

save_directory (str or os.PathLike) – Directory where the configuration JSON file will be saved (will be created if it does not exist).
push_to_hub (bool, optional, defaults to False) – 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).
kwargs (Dict[str, Any], optional) – Additional key word arguments passed along to the [~utils.PushToHubMixin.push_to_hub] method.

static _set_token_in_kwargs(kwargs, token=None)

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.

classmethod from_pretrained(pretrained_model_name_or_path, cache_dir=None, force_download=False, local_files_only=False, token=None, revision='main', **kwargs)

Instantiate a [PretrainedConfig] (or a derived class) from a pretrained model configuration.

Parameters:

pretrained_model_name_or_path (str or os.PathLike) –
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.
cache_dir (str or os.PathLike, optional) – Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
force_download (bool, optional, defaults to False) – Whether or not to force to (re-)download the configuration files and override the cached versions if they exist.
resume_download – Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
proxies (Dict[str, str], optional) – 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.
token (str or bool, optional) – 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).
revision (str, optional, defaults to “main”) –
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.

<Tip>

To test a pull request you made on the Hub, you can pass revision=”refs/pr/<pr_number>”.

</Tip>
return_unused_kwargs (bool, optional, defaults to False) –
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.
subfolder (str, optional, defaults to “”) – In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here.
kwargs (Dict[str, Any], optional) – 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.
local_files_only (bool)

Returns:

[`PretrainedConfig`] – The configuration object instantiated from this pretrained model.

Return type:

PretrainedConfig

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} ```

classmethod get_config_dict(pretrained_model_name_or_path, **kwargs)

From a pretrained_model_name_or_path, resolve to a dictionary of parameters, to be used for instantiating a [PretrainedConfig] using from_dict.

Parameters:: pretrained_model_name_or_path (str or os.PathLike) – The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
Returns:: `Tuple[Dict, Dict]` – The dictionary(ies) that will be used to instantiate the configuration object.
Return type:: Tuple[Dict[str, Any], Dict[str, Any]]

classmethod _get_config_dict(pretrained_model_name_or_path, **kwargs)

Parameters:: pretrained_model_name_or_path (Union[str, os.PathLike])
Return type:: Tuple[Dict[str, Any], Dict[str, Any]]

classmethod from_dict(config_dict, **kwargs)

Instantiates a [PretrainedConfig] from a Python dictionary of parameters.

Parameters:

config_dict (Dict[str, Any]) – 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.
kwargs (Dict[str, Any]) – Additional parameters from which to initialize the configuration object.

Returns:

[`PretrainedConfig`] – The configuration object instantiated from those parameters.

Return type:

PretrainedConfig

classmethod from_json_file(json_file)

Instantiates a [PretrainedConfig] from the path to a JSON file of parameters.

Parameters:: json_file (str or os.PathLike) – Path to the JSON file containing the parameters.
Returns:: [`PretrainedConfig`] – The configuration object instantiated from that JSON file.
Return type:: PretrainedConfig

classmethod _dict_from_json_file(json_file)

Parameters:: json_file (Union[str, os.PathLike])

__eq__(other)

__repr__()

__iter__()

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,
Return type:: Dict[str, Any]

to_json_string(use_diff=True)

Serializes this instance to a JSON string.

Parameters:: use_diff (bool, optional, defaults to True) – If set to True, only the difference between the config instance and the default PretrainedConfig() is serialized to JSON string.
Returns:: `str` – String containing all the attributes that make up this configuration instance in JSON format.
Return type:: str

to_json_file(json_file_path, use_diff=True)

Save this instance to a JSON file.

Parameters:

json_file_path (str or os.PathLike) – Path to the JSON file in which this configuration instance’s parameters will be saved.
use_diff (bool, optional, defaults to True) – If set to True, only the difference between the config instance and the default PretrainedConfig() is serialized to JSON file.

update(config_dict)

Updates attributes of this class with attributes from config_dict.

Parameters:: config_dict (Dict[str, Any]) – Dictionary of attributes that should be updated for this class.

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.

Parameters:: update_str (str) – String with attributes that should be updated for this class.

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.

Parameters:: d (Dict[str, Any])
Return type:: None

classmethod register_for_auto_class(auto_class='AutoConfig')

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.

</Tip>

Parameters:: auto_class (str or type, optional, defaults to “AutoConfig”) – The auto class to register this new configuration with.

static _get_global_generation_defaults()

Return type:: Dict[str, Any]

_get_non_default_generation_parameters()

Gets the non-default generation parameters on the PretrainedConfig instance

Return type:: Dict[str, Any]

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.

Return type:: PretrainedConfig

_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.

Parameters:

repo_id (str)
private (Optional[bool])
token (Optional[Union[bool, str]])
repo_url (Optional[str])
organization (Optional[str])

Return type:

str

_get_files_timestamps(working_dir)

Returns the list of files with their last modification timestamp.

Parameters:: working_dir (Union[str, os.PathLike])

_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.

Parameters:

working_dir (Union[str, os.PathLike])
repo_id (str)
files_timestamps (Dict[str, float])
commit_message (Optional[str])
token (Optional[Union[bool, str]])
create_pr (bool)
revision (str)
commit_description (str)

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.

Parameters:

repo_id (str) – The name of the repository you want to push your {object} to. It should contain your organization name when pushing to a given organization.
use_temp_dir (bool, optional) – 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.
commit_message (str, optional) – Message to commit while pushing. Will default to “Upload {object}”.
private (bool, optional) – 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.
token (bool or str, optional) – 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.
max_shard_size (int or str, optional, defaults to “5GB”) – 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.
create_pr (bool, optional, defaults to False) – Whether or not to create a PR with the uploaded files or directly commit.
safe_serialization (bool, optional, defaults to True) – Whether or not to convert the model weights in safetensors format for safer serialization.
revision (str, optional) – Branch to push the uploaded files to.
commit_description (str, optional) – The description of the commit that will be created
tags (List[str], optional) – List of tags to push on the Hub.

Return type:

str

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”) ```

medcat.components.addons.relation_extraction.base_component.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()

Parameters:

model (RelExtrBaseModel) – RelExtrBaseModel, it has to be initialized before calling this method via RelExtr(Bert/Llama)Model(…)
optimizer (_type_) – optimizer
scheduler (_type_) – scheduler
path (str, optional) – Defaults to “./”.
model_name (str, optional) – Defaults to “BERT”.
file_prefix (str, optional) – Defaults to “train”.
load_best (bool, optional) – Defaults to False.
relcat_config (ConfigRelCAT) – Defaults to ConfigRelCAT().

Returns:

tuple (int, int) – last epoch and f1 score.

Return type:

tuple[int, int]

medcat.components.addons.relation_extraction.base_component.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.

Parameters:

model (BaseModel) – BertMode | LlamaModel etc.
optimizer (torch.optim.AdamW, optional) – Defaults to None.
scheduler (torch.optim.lr_scheduler.MultiStepLR, optional) – Defaults to None.
epoch (int) – Defaults to None.
best_f1 (float) – Defaults to None.
path (str) – Defaults to “./”.
model_name (str) – . Defaults to “BERT”. This is used to checkpointing only.
task (str) – Defaults to “train”. This is used to checkpointing only.
is_checkpoint (bool) – Defaults to False.
final_export (bool) – Defaults to False, if True then is_checkpoint must be False also. Exports model.state_dict(), out into “model.dat”.

Return type:

None

medcat.components.addons.relation_extraction.base_component.logger

class medcat.components.addons.relation_extraction.base_component.RelExtrBaseComponent(tokenizer=BaseTokenizerWrapper(), model=None, model_config=None, config=ConfigRelCAT(), task='train', init_model=False)

Parameters:

tokenizer (medcat.components.addons.relation_extraction.tokenizer.BaseTokenizerWrapper)
model (medcat.components.addons.relation_extraction.models.RelExtrBaseModel)
model_config (medcat.components.addons.relation_extraction.config.RelExtrBaseConfig)
config (medcat.config.config_rel_cat.ConfigRelCAT)
task (str)
init_model (bool)

name = 'base_component_rel'

__init__(tokenizer=BaseTokenizerWrapper(), model=None, model_config=None, config=ConfigRelCAT(), task='train', init_model=False)

Component that holds the model and everything for RelCAT.

Parameters:

tokenizer (BaseTokenizerWrapper) – The base tokenizer for RelCAT.
model (RelExtrBaseModel) – The model wrapper.
model_config (RelExtrBaseConfig) – The model-specific config.
config (ConfigRelCAT) – The RelCAT config.
task (str) – The task - used for checkpointing.
init_model (bool) – Loads default BERT base model, tokenizer, model config. Defaults to False.

model: medcat.components.addons.relation_extraction.models.RelExtrBaseModel = None

tokenizer: medcat.components.addons.relation_extraction.tokenizer.BaseTokenizerWrapper

relcat_config: medcat.config.config_rel_cat.ConfigRelCAT

model_config: medcat.components.addons.relation_extraction.config.RelExtrBaseConfig = None

optimizer: torch.optim.AdamW = None

scheduler: torch.optim.lr_scheduler.MultiStepLR = None

task: str = 'train'

epoch: int = 0

best_f1: float = 0.0

pad_id

padding_seq

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.

Parameters:: save_path (str) – folder path in which to save the model & deps.
Return type:: None

classmethod load(pretrained_model_name_or_path='./')

Parameters:: pretrained_model_name_or_path (str) – Path to RelCAT model. Defaults to “./”.
Returns:: RelExtrBaseComponent – component.
Return type:: RelExtrBaseComponent

classmethod from_relcat_config(relcat_config, pretrained_model_name_or_path='./')

Parameters:

relcat_config (medcat.config.config_rel_cat.ConfigRelCAT)
pretrained_model_name_or_path (str)

Return type:

RelExtrBaseComponent