# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import json
import math
import os
import copy
import csv
import itertools
from .utils import download_file
from .utils import TermTree
from .knowledge_mining import WordTagTask
from .lexical_analysis import LacTask
from .utils import Customization
POS_LABEL_WORDTAG = [
"介词", "介词_方位介词", "助词", "代词", "连词", "副词", "疑问词", "肯定词", "否定词", "数量词", "叹词",
"拟声词", "修饰词", "外语单词", "英语单词", "汉语拼音", "词汇用语", "w"
]
POS_LABEL_LAC = [
"n", "f", "s", "t", "v", "vd", "vn", "a", "ad", "an", "d", "m", "q", "r",
"p", "c", "u", "xc", "w"
]
usage = r"""
from paddlenlp import Taskflow
# WordTag精确模式
ner = Taskflow("ner")
ner("《孤女》是2010年九州出版社出版的小说，作者是余兼羽")
'''
[('《', 'w'), ('孤女', '作品类_实体'), ('》', 'w'), ('是', '肯定词'), ('2010年', '时间类'), ('九州出版社', '组织机构类'), ('出版', '场景事件'), ('的', '助词'), ('小说', '作品类_概念'), ('，', 'w'), ('作者', '人物类_概念'), ('是', '肯定词'), ('余兼羽', '人物类_实体')]
'''
ner(["热梅茶是一道以梅子为主要原料制作的茶饮", "《孤女》是2010年九州出版社出版的小说，作者是余兼羽"])
'''
[[('热梅茶', '饮食类_饮品'), ('是', '肯定词'), ('一道', '数量词'), ('以', '介词'), ('梅子', '饮食类'), ('为', '肯定词'), ('主要原料', '物体类'), ('制作', '场景事件'), ('的', '助词'), ('茶饮', '饮食类_饮品')], [('《', 'w'), ('孤女', '作品类_实体'), ('》', 'w'), ('是', '肯定词'), ('2010年', '时间类'), ('九州出版社', '组织机构类'), ('出版', '场景事件'), ('的', '助词'), ('小说', '作品类_概念'), ('，', 'w'), ('作者', '人物类_概念'), ('是', '肯定词'), ('余兼羽', '人物类_实体')]]
'''
# 只返回实体/概念词
ner = Taskflow("ner", entity_only=True)
ner("《孤女》是2010年九州出版社出版的小说，作者是余兼羽")
'''
[('孤女', '作品类_实体'), ('2010年', '时间类'), ('九州出版社', '组织机构类'), ('出版', '场景事件'), ('小说', '作品类_概念'), ('作者', '人物类_概念'), ('余兼羽', '人物类_实体')]
'''
# 使用快速模式，只返回实体词
ner = Taskflow("ner", mode="fast", entity_only=True)
ner("三亚是一个美丽的城市")
'''
[('三亚', 'LOC')]
'''
"""
[文档]class NERWordTagTask(WordTagTask):
"""
This the NER(Named Entity Recognition) task that convert the raw text to entities. And the task with the `wordtag`
model will link the more meesage with the entity.
Args:
task(string): The name of task.
model(string): The model name in the task.
kwargs (dict, optional): Additional keyword arguments passed along to the specific task.
"""
resource_files_names = {
"model_state": "model_state.pdparams",
"model_config": "model_config.json",
"tags": "tags.txt",
}
resource_files_urls = {
"wordtag": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/knowledge_mining/wordtag/model_state.pdparams",
"12685d1d84c09fb851b6c1541af1146e"
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/knowledge_mining/wordtag/model_config.json",
"aa47cdf7c270943a24495bd5ff59dc00"
],
"tags": [
"https://bj.bcebos.com/paddlenlp/taskflow/knowledge_mining/wordtag/tags.txt",
"87db06ae6ca42565157045ab3e9a996f"
],
}
}
def __init__(self, model, task, entity_only=False, **kwargs):
super().__init__(model="wordtag", task=task, **kwargs)
self.entity_only = entity_only
if self._user_dict:
self._custom = Customization()
self._custom.load_customization(self._user_dict)
else:
self._custom = None
def _decode(self, batch_texts, batch_pred_tags):
batch_results = []
for sent_index in range(len(batch_texts)):
sent = batch_texts[sent_index]
indexes = batch_pred_tags[sent_index][self.summary_num:len(sent) +
self.summary_num]
tags = [self._index_to_tags[index] for index in indexes]
if self._custom:
self._custom.parse_customization(sent, tags, prefix=True)
sent_out = []
tags_out = []
partial_word = ""
for ind, tag in enumerate(tags):
if partial_word == "":
partial_word = sent[ind]
tags_out.append(tag.split('-')[-1])
continue
if tag.startswith("B") or tag.startswith("S") or tag.startswith(
"O"):
sent_out.append(partial_word)
tags_out.append(tag.split('-')[-1])
partial_word = sent[ind]
continue
partial_word += sent[ind]
if len(sent_out) < len(tags_out):
sent_out.append(partial_word)
pred_words = []
for s, t in zip(sent_out, tags_out):
pred_words.append({"item": s, "wordtag_label": t})
result = {"text": sent, "items": pred_words}
batch_results.append(result)
return batch_results
def _simplify_result(self, results):
simple_results = []
for result in results:
simple_result = []
if 'items' in result:
for item in result['items']:
if self.entity_only and item[
'wordtag_label'] in POS_LABEL_WORDTAG:
continue
simple_result.append((item['item'], item['wordtag_label']))
simple_results.append(simple_result)
simple_results = simple_results[0] if len(
simple_results) == 1 else simple_results
return simple_results
def _postprocess(self, inputs):
"""
The model output is the tag ids, this function will convert the model output to raw text.
"""
results = self._decode(inputs['short_input_texts'],
inputs['all_pred_tags'])
results = self._auto_joiner(results, self.input_mapping, is_dict=True)
results = self._simplify_result(results)
return results
[文档]class NERLACTask(LacTask):
"""
Part-of-speech tagging task for the raw text.
Args:
task(string): The name of task.
model(string): The model name in the task.
kwargs (dict, optional): Additional keyword arguments passed along to the specific task.
"""
def __init__(self, model, task, entity_only=False, **kwargs):
super().__init__(task=task, model="lac", **kwargs)
self.entity_only = entity_only
def _postprocess(self, inputs):
"""
The model output is the tag ids, this function will convert the model output to raw text.
"""
batch_out = []
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)
result = []
for s, t in zip(sent_out, tags_out):
if self.entity_only and t in POS_LABEL_LAC:
continue
result.append((s, t))
final_results.append(result)
final_results = self._auto_joiner(final_results, self.input_mapping)
final_results = final_results if len(
final_results) > 1 else final_results[0]
return final_results