Function LTP

python/interface/ltp/interface.py:13–145

r""" Instantiate a pretrained LTP model from a pre-trained model configuration from huggingface-hub. The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train the model, you should first set it

(
    pretrained_model_name_or_path="LTP/small",
    force_download: bool = False,
    resume_download: bool = False,
    proxies: Dict = None,
    use_auth_token: Optional[str] = None,
    cache_dir: Optional[str] = None,
    local_files_only: bool = False,
    **model_kwargs,
)

def LTP(
    pretrained_model_name_or_path="LTP/small",
    force_download: bool = False,
    resume_download: bool = False,
    proxies: Dict = None,
    use_auth_token: Optional[str] = None,
    cache_dir: Optional[str] = None,
    local_files_only: bool = False,
    **model_kwargs,
) -> Union[LTP_legacy, LTP_neural]:
    r"""
    Instantiate a pretrained LTP model from a pre-trained model
            configuration from huggingface-hub. The model is set in
            evaluation mode by default using `model.eval()` (Dropout modules
            are deactivated). To train the model, you should first set it
            back in training mode with `model.train()`.

            Parameters:
                pretrained_model_name_or_path (`str` or `os.PathLike`):
                    Can be either:
                        - A string, the `model id` of a pretrained model
                          hosted inside a model repo on huggingface.co.
                          Valid model ids are [`LTP/tiny`, `LTP/small`,
                          `LTP/base`, `LTP/base1`, `LTP/base1`, `LTP/legacy`
                          ], the legacy model only support cws, pos and ner,
                          but more fast.
                        - You can add `revision` by appending `@` at the end
                          of model_id simply like this:
                          `dbmdz/bert-base-german-cased@main` Revision is
                          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.
                        - A path to a `directory` containing model weights
                          saved using
                          [`~transformers.PreTrainedModel.save_pretrained`],
                          e.g., `./my_model_directory/`.
                        - `None` if you are both providing the configuration
                          and state dictionary (resp. with keyword arguments
                          `config` and `state_dict`).
                force_download (`bool`, *optional*, defaults to `False`):
                    Whether to force the (re-)download of the model weights
                    and configuration files, overriding the cached versions
                    if they exist.
                resume_download (`bool`, *optional*, defaults to `False`):
                    Whether to delete incompletely received files. Will
                    attempt to resume the download if such a file exists.
                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.
                use_auth_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 `transformers-cli login` (stored in
                    `~/.huggingface`).