LTP 文档¶

Submodules¶

ltp.interface module¶

ltp.interface.LTP(pretrained_model_name_or_path='LTP/small', force_download=False, proxies=None, use_auth_token=None, cache_dir=None, local_files_only=False, **model_kwargs)[source]¶

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/base2, 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.

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).

cache_dir (Union[str, 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.

local_files_only(bool, optional, defaults to False):

Whether to only look at local files (i.e., do not try to download the model).

model_kwargs (Dict, optional):

model_kwargs will be passed to the model during initialization

<Tip>

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

</Tip>

ltp.legacy module¶

class ltp.legacy.LTP(cws=None, pos=None, ner=None)[source]¶

Bases: ModelHubMixin

property version¶

add_word(word, freq=1)[source]¶

add_words(words, freq=2)[source]¶

enable_type_cut(a, b)[source]¶

enable_type_cut_d(a, b)[source]¶

enable_type_concat(a, b)[source]¶

enable_type_concat_d(a, b)[source]¶

disable_rule(a, b)[source]¶

disable_rule_d(a, b)[source]¶

pipeline(*args, tasks=None, raw_format=False, parallelism=True, return_dict=True)[source]¶

auto_hook(words)[source]¶

ltp.legacy.main()[source]¶

ltp.nerual module¶

ltp.nerual.no_grad(func)[source]¶

class ltp.nerual.LTP(config=None, tokenizer=None)[source]¶

Bases: BaseModule, ModelHubMixin

model¶

cws_vocab¶

pos_vocab¶

ner_vocab¶

srl_vocab¶

dep_vocab¶

sdp_vocab¶

add_word(word, freq=1)[source]¶

add_words(words, freq=2)[source]¶

load_state_dict(state_dict, strict=True)[source]¶

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

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.

Return type

NamedTuple with missing_keys and unexpected_keys fields

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.

property version¶

pipeline(**kwargs)[source]¶

static get_graph_entities(rarcs, rels, labels)[source]¶

ltp.nerual.main()[source]¶

Module contents¶

ltp.LTP(pretrained_model_name_or_path='LTP/small', force_download=False, proxies=None, use_auth_token=None, cache_dir=None, local_files_only=False, **model_kwargs)[source]¶