load()
Load documents.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(path: str, page_content_column: str = 'text', name: Optional[str] = None, data_dir: Optional[str] = None, data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None, cache_dir: Optional[str] = None, keep_in_memory: Optional[bool] = None, save_infos: bool = False, use_auth_token: Optional[Union[bool, str]] = None, num_proc: Optional[int] = None)[source]¶
Initialize the HuggingFaceDatasetLoader.
Parameters
path – Path or name of the dataset.
page_content_column – Page content column name. Default is “text”.
Note: Currently the function assumes the content is a string.
If it is not download the dataset using huggingface library and convert
using the json or pandas loaders.
https://github.com/langchain-ai/langchain/issues/10674
name – Name of the dataset configuration.
data_dir – Data directory of the dataset configuration.
data_files – Path(s) to source data file(s).
cache_dir – Directory to read/write data.
keep_in_memory – Whether to copy the dataset in-memory.
save_infos – Save the dataset information (checksums/size/splits/…).
Default is False.
use_auth_token – Bearer token for remote files on the Dataset Hub.
num_proc – Number of processes.
lazy_load() → Iterator[Document][source]¶
Load documents lazily.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters