`langchain_community.utilities.tensorflow_datasets`.TensorflowDatasets¶

class langchain_community.utilities.tensorflow_datasets.TensorflowDatasets[source]¶

Bases: BaseModel

Access to the TensorFlow Datasets.

The Current implementation can work only with datasets that fit in a memory.

TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets. To get started see the Guide: https://www.tensorflow.org/datasets/overview and the list of datasets: https://www.tensorflow.org/datasets/catalog/

overview#all_datasets

You have to provide the sample_to_document_function: a function that: a sample from the dataset-specific format to the Document.

dataset_name¶: the name of the dataset to load

split_name¶: the name of the split to load. Defaults to “train”.

load_max_docs¶: a limit to the number of loaded documents. Defaults to 100.

sample_to_document_function¶: a function that converts a dataset sample to a Document

Example

from langchain_community.utilities import TensorflowDatasets

def mlqaen_example_to_document(example: dict) -> Document:
    return Document(
        page_content=decode_to_str(example["context"]),
        metadata={
            "id": decode_to_str(example["id"]),
            "title": decode_to_str(example["title"]),
            "question": decode_to_str(example["question"]),
            "answer": decode_to_str(example["answers"]["text"][0]),
        },
    )

tsds_client = TensorflowDatasets(
        dataset_name="mlqa/en",
        split_name="train",
        load_max_docs=MAX_DOCS,
        sample_to_document_function=mlqaen_example_to_document,
    )

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param dataset_name: str = ''¶

param load_max_docs: int = 100¶

param sample_to_document_function: Optional[Callable[[Dict], langchain_core.documents.base.Document]] = None¶

param split_name: str = 'train'¶

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

lazy_load() → Iterator[Document][source]¶

Download a selected dataset lazily.

Returns: an iterator of Documents.

load() → List[Document][source]¶

Download a selected dataset.

Returns: a list of Documents.

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

langchain_community.utilities.tensorflow_datasets.TensorflowDatasets¶

`langchain_community.utilities.tensorflow_datasets`.TensorflowDatasets¶