Source code for paddlenlp.taskflow.lexical_analysis

# coding:utf-8
# Copyright (c) 2021  PaddlePaddle Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import os

import paddle

from import Pad, Stack, Tuple
from ..datasets import load_dataset
from .models import BiGruCrf
from .task import Task
from .utils import Customization

usage = r"""
           from paddlenlp import Taskflow

           lac = Taskflow("lexical_analysis")
           [{'text': 'LAC是个优秀的分词工具', 'segs': ['LAC', '是', '个', '优秀', '的', '分词', '工具'], 'tags': ['nz', 'v', 'q', 'a', 'u', 'n', 'n']}]

           lac(["LAC是个优秀的分词工具", "三亚是一个美丽的城市"])
           [{'text': 'LAC是个优秀的分词工具', 'segs': ['LAC', '是', '个', '优秀', '的', '分词', '工具'], 'tags': ['nz', 'v', 'q', 'a', 'u', 'n', 'n']},
            {'text': '三亚是一个美丽的城市', 'segs': ['三亚', '是', '一个', '美丽', '的', '城市'], 'tags': ['LOC', 'v', 'm', 'a', 'u', 'n']}


[docs] def load_vocab(dict_path): """ Load vocab from file """ vocab = {} reverse = None with open(dict_path, "r", encoding="utf8") as fin: for i, line in enumerate(fin): terms = line.strip("\n").split("\t") if len(terms) == 2: if reverse is None: reverse = True if terms[0].isdigit() else False if reverse: value, key = terms else: key, value = terms elif len(terms) == 1: key, value = terms[0], str(i) else: raise ValueError("Error line: %s in file: %s" % (line, dict_path)) vocab[key] = value return vocab
[docs] class LacTask(Task): """ Lexical analysis of Chinese task to segement the chinese sentence. Args: task(string): The name of task. model(string): The model name in the task. user_dict(string): The user-defined dictionary, default to None. kwargs (dict, optional): Additional keyword arguments passed along to the specific task. """ resource_files_names = { "model_state": "model_state.pdparams", "tags": "tag.dic", "q2b": "q2b.dic", "word": "word.dic", } resource_files_urls = { "lac": { "model_state": [ "", "3d4008c6c9d29424465829c9acf909bd", ], "tags": [ "", "b11b616926b9f7f0a40a8087f84a8a99", ], "q2b": [ "", "4ef2cd16f8002fe7cd7dd31cdff47e0d", ], "word": [ "", "f1dfc68139bb6dd58c9c4313c341e436", ], } } def __init__(self, task, model, user_dict=None, **kwargs): super().__init__(task=task, model=model, **kwargs) self._usage = usage self._user_dict = user_dict self._check_task_files() self._construct_vocabs() self._get_inference_model() self._max_seq_len = 512 if self._user_dict: self._custom = Customization() self._custom.load_customization(self._user_dict) else: self._custom = None def _construct_input_spec(self): """ Construct the input spec for the convert dygraph model to static model. """ self._input_spec = [ paddle.static.InputSpec(shape=[None, None], dtype="int64", name="token_ids"), paddle.static.InputSpec(shape=[None], dtype="int64", name="length"), ] def _construct_vocabs(self): word_dict_path = os.path.join(self._task_path, "word.dic") tag_dict_path = os.path.join(self._task_path, "tag.dic") q2b_dict_path = os.path.join(self._task_path, "q2b.dic") self._word_vocab = load_vocab(word_dict_path) self._tag_vocab = load_vocab(tag_dict_path) self._q2b_vocab = load_vocab(q2b_dict_path) self._id2word_dict = dict(zip(self._word_vocab.values(), self._word_vocab.keys())) self._id2tag_dict = dict(zip(self._tag_vocab.values(), self._tag_vocab.keys())) def _construct_model(self, model): """ Construct the inference model for the predictor. """ model_instance = BiGruCrf( self.kwargs["emb_dim"], self.kwargs["hidden_size"], len(self._word_vocab), len(self._tag_vocab) ) # Load the model parameter for the predict state_dict = paddle.load(os.path.join(self._task_path, "model_state.pdparams")) model_instance.set_dict(state_dict) self._model = model_instance self._model.eval() def _construct_tokenizer(self, model): """ Construct the tokenizer for the predictor. """ return None def _preprocess(self, inputs, padding=True, add_special_tokens=True): """ Transform the raw text to the model inputs, two steps involved: 1) Transform the raw text to token ids. 2) Generate the other model inputs from the raw text and token ids. """ inputs = self._check_input_text(inputs) # Get the config from the kwargs batch_size = self.kwargs["batch_size"] if "batch_size" in self.kwargs else 1 num_workers = self.kwargs["num_workers"] if "num_workers" in self.kwargs else 0 self._split_sentence = self.kwargs["split_sentence"] if "split_sentence" in self.kwargs else False oov_token_id = self._word_vocab.get("OOV") filter_inputs = [] for input in inputs: if not (isinstance(input, str) and len(input.strip()) > 0): continue filter_inputs.append(input) short_input_texts, self.input_mapping = self._auto_splitter( filter_inputs, self._max_seq_len, split_sentence=self._split_sentence ) def read(inputs): for input_tokens in inputs: ids = [] for token in input_tokens: token = self._q2b_vocab.get(token, token) token_id = self._word_vocab.get(token, oov_token_id) ids.append(token_id) lens = len(ids) yield ids, lens infer_ds = load_dataset(read, inputs=short_input_texts, lazy=False) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=0, dtype="int64"), # input_ids Stack(dtype="int64"), # seq_len ): fn(samples) infer_data_loader = infer_ds, collate_fn=batchify_fn, num_workers=num_workers, batch_size=batch_size, shuffle=False, return_list=True, ) outputs = {} outputs["text"] = short_input_texts outputs["data_loader"] = infer_data_loader return outputs def _run_model(self, inputs): """ Run the task model from the outputs of the `_tokenize` function. """ results = [] lens = [] for batch in inputs["data_loader"]: input_ids, seq_len = batch self.input_handles[0].copy_from_cpu(input_ids.numpy()) self.input_handles[1].copy_from_cpu(seq_len.numpy()) tags_ids = self.output_handle[0].copy_to_cpu() results.extend(tags_ids.tolist()) lens.extend(seq_len.tolist()) inputs["result"] = results inputs["lens"] = lens return inputs def _postprocess(self, inputs): """ The model output is the tag ids, this function will convert the model output to raw text. """ lengths = inputs["lens"] preds = inputs["result"] sents = inputs["text"] final_results = [] for sent_index in range(len(lengths)): single_result = {} tags = [self._id2tag_dict[str(index)] for index in preds[sent_index][: lengths[sent_index]]] sent = sents[sent_index] if self._custom: self._custom.parse_customization(sent, tags) sent_out = [] tags_out = [] parital_word = "" for ind, tag in enumerate(tags): if parital_word == "": parital_word = sent[ind] tags_out.append(tag.split("-")[0]) continue if tag.endswith("-B") or (tag == "O" and tags[ind - 1] != "O"): sent_out.append(parital_word) tags_out.append(tag.split("-")[0]) parital_word = sent[ind] continue parital_word += sent[ind] if len(sent_out) < len(tags_out): sent_out.append(parital_word) single_result["text"] = sent single_result["segs"] = sent_out single_result["tags"] = tags_out final_results.append(single_result) final_results = self._auto_joiner(final_results, self.input_mapping, is_dict=True) return final_results