medcat.components.addons.relation_extraction.llama.config

Attributes

logger

Classes

`ConfigRelCAT`	The RelCAT part of the config
`RelExtrBaseConfig`	Base class for the RelCAT models
`RelExtrLlamaConfig`	Class for LlamaConfig

Module Contents

class medcat.components.addons.relation_extraction.llama.config.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

class medcat.components.addons.relation_extraction.llama.config.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.llama.config.logger

class medcat.components.addons.relation_extraction.llama.config.RelExtrLlamaConfig(pretrained_model_name_or_path, **kwargs)

Bases: medcat.components.addons.relation_extraction.config.RelExtrBaseConfig

Class for LlamaConfig

name = 'llama-config'

pretrained_model_name_or_path = 'meta-llama/Llama-3.1-8B'

hf_model_config: transformers.LlamaConfig

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:

RelExtrLlamaConfig

__init__(pretrained_model_name_or_path, **kwargs)

model_type = 'relcat'

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)

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”) ```