# coding:utf-8
# Copyright (c) 2022 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 re
import os
import json
import numpy as np
import paddle
from ..datasets import load_dataset
from ..transformers import AutoTokenizer, AutoModel
from ..layers import GlobalPointerForEntityExtraction, GPLinkerForRelationExtraction
from .models import UIE, UIEM
from .task import Task
from .utils import SchemaTree, get_span, get_id_and_prob, get_bool_ids_greater_than, dbc2sbc, gp_decode, DataCollatorGP
usage = r"""
from paddlenlp import Taskflow
# Entity Extraction
schema = ['时间', '选手', '赛事名称'] # Define the schema for entity extraction
ie = Taskflow('information_extraction', schema=schema)
ie("2月8日上午北京冬奥会自由式滑雪女子大跳台决赛中中国选手谷爱凌以188.25分获得金牌！")
'''
[{'时间': [{'text': '2月8日上午', 'start': 0, 'end': 6, 'probability': 0.9857378532924486}], '选手': [{'text': '谷爱凌', 'start': 28, 'end': 31, 'probability': 0.8981548639781138}], '赛事名称': [{'text': '北京冬奥会自由式滑雪女子大跳台决赛', 'start': 6, 'end': 23, 'probability': 0.8503089953268272}]}]
'''
# Relation Extraction
schema = [{"歌曲名称":["歌手", "所属专辑"]}] # Define the schema for relation extraction
ie.set_schema(schema) # Reset schema
ie("《告别了》是孙耀威在专辑爱的故事里面的歌曲")
'''
[{'歌曲名称': [{'text': '告别了', 'start': 1, 'end': 4, 'probability': 0.6296155977145546, 'relations': {'歌手': [{'text': '孙耀威', 'start': 6, 'end': 9, 'probability': 0.9988381005599081}], '所属专辑': [{'text': '爱的故事', 'start': 12, 'end': 16, 'probability': 0.9968462078543183}]}}, {'text': '爱的故事', 'start': 12, 'end': 16, 'probability': 0.2816869478191606, 'relations': {'歌手': [{'text': '孙耀威', 'start': 6, 'end': 9, 'probability': 0.9951415104192272}]}}]}]
'''
# Event Extraction
schema = [{'地震触发词': ['地震强度', '时间', '震中位置', '震源深度']}] # Define the schema for event extraction
ie.set_schema(schema) # Reset schema
ie('中国地震台网正式测定：5月16日06时08分在云南临沧市凤庆县(北纬24.34度，东经99.98度)发生3.5级地震，震源深度10千米。')
'''
[{'地震触发词': [{'text': '地震', 'start': 56, 'end': 58, 'probability': 0.9977425555988333, 'relations': {'地震强度': [{'text': '3.5级', 'start': 52, 'end': 56, 'probability': 0.998080217831891}], '时间': [{'text': '5月16日06时08分', 'start': 11, 'end': 22, 'probability': 0.9853299772936026}], '震中位置': [{'text': '云南临沧市凤庆县(北纬24.34度，东经99.98度)', 'start': 23, 'end': 50, 'probability': 0.7874012889740385}], '震源深度': [{'text': '10千米', 'start': 63, 'end': 67, 'probability': 0.9937974422968665}]}}]}]
'''
# Opinion Extraction
schema = [{'评价维度': ['观点词', '情感倾向[正向，负向]']}] # Define the schema for opinion extraction
ie.set_schema(schema) # Reset schema
ie("地址不错，服务一般，设施陈旧")
'''
[{'评价维度': [{'text': '地址', 'start': 0, 'end': 2, 'probability': 0.9888139270606509, 'relations': {'观点词': [{'text': '不错', 'start': 2, 'end': 4, 'probability': 0.9927847072459528}], '情感倾向[正向，负向]': [{'text': '正向', 'probability': 0.998228967796706}]}}, {'text': '设施', 'start': 10, 'end': 12, 'probability': 0.9588297379365116, 'relations': {'观点词': [{'text': '陈旧', 'start': 12, 'end': 14, 'probability': 0.9286753967902683}], '情感倾向[正向，负向]': [{'text': '负向', 'probability': 0.9949389795770394}]}}, {'text': '服务', 'start': 5, 'end': 7, 'probability': 0.9592857070501211, 'relations': {'观点词': [{'text': '一般', 'start': 7, 'end': 9, 'probability': 0.9949359182521675}], '情感倾向[正向，负向]': [{'text': '负向', 'probability': 0.9952498258302498}]}}]}]
'''
# Sentence-level Sentiment Classification
schema = ['情感倾向[正向，负向]'] # Define the schema for sentence-level sentiment classification
ie.set_schema(schema) # Reset schema
ie('这个产品用起来真的很流畅，我非常喜欢')
'''
[{'情感倾向[正向，负向]': [{'text': '正向', 'probability': 0.9990024058203417}]}]
'''
# English Model
schema = [{'Person': ['Company', 'Position']}]
ie_en = Taskflow('information_extraction', schema=schema, model='uie-base-en')
ie_en('In 1997, Steve was excited to become the CEO of Apple.')
'''
[{'Person': [{'text': 'Steve', 'start': 9, 'end': 14, 'probability': 0.999631971804547, 'relations': {'Company': [{'text': 'Apple', 'start': 48, 'end': 53, 'probability': 0.9960158209451642}], 'Position': [{'text': 'CEO', 'start': 41, 'end': 44, 'probability': 0.8871063806420736}]}}]}]
'''
schema = ['Sentiment classification [negative, positive]']
ie_en.set_schema(schema)
ie_en('I am sorry but this is the worst film I have ever seen in my life.')
'''
[{'Sentiment classification [negative, positive]': [{'text': 'negative', 'probability': 0.9998415771287057}]}]
'''
# Multilingual Model
schema = [{'Person': ['Company', 'Position']}]
ie_m = Taskflow('information_extraction', schema=schema, model='uie-m-base', schema_lang="en")
ie_m('In 1997, Steve was excited to become the CEO of Apple.')
'''
[{'Person': [{'text': 'Steve', 'start': 9, 'end': 14, 'probability': 0.9998436034905893, 'relations': {'Company': [{'text': 'Apple', 'start': 48, 'end': 53, 'probability': 0.9842775467359672}], 'Position': [{'text': 'CEO', 'start': 41, 'end': 44, 'probability': 0.9628799853543271}]}}]}]
'''
"""
[文档]class UIETask(Task):
"""
Universal Information Extraction Task.
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",
"vocab_file": "vocab.txt",
"special_tokens_map": "special_tokens_map.json",
"tokenizer_config": "tokenizer_config.json",
}
# vocab.txt/special_tokens_map.json/tokenizer_config.json are common to the default model.
resource_files_urls = {
"uie-base": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_v1.0/model_state.pdparams",
"aeca0ed2ccf003f4e9c6160363327c9b",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/model_config.json",
"a36c185bfc17a83b6cfef6f98b29c909",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-medium": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medium_v1.0/model_state.pdparams",
"15874e4e76d05bc6de64cc69717f172e",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medium/model_config.json",
"6f1ee399398d4f218450fbbf5f212b15",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-mini": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_mini_v1.0/model_state.pdparams",
"f7b493aae84be3c107a6b4ada660ce2e",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_mini/model_config.json",
"9229ce0a9d599de4602c97324747682f",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-micro": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_micro_v1.0/model_state.pdparams",
"80baf49c7f853ab31ac67802104f3f15",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_micro/model_config.json",
"07ef444420c3ab474f9270a1027f6da5",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-nano": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_nano_v1.0/model_state.pdparams",
"ba934463c5cd801f46571f2588543700",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_nano/model_config.json",
"e3a9842edf8329ccdd0cf6039cf0a8f8",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
# Rename to `uie-medium` and the name of `uie-tiny` will be deprecated in future.
"uie-tiny": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_tiny_v0.1/model_state.pdparams",
"15874e4e76d05bc6de64cc69717f172e",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_tiny/model_config.json",
"6f1ee399398d4f218450fbbf5f212b15",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-medical-base": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medical_base_v0.1/model_state.pdparams",
"569b4bc1abf80eedcdad5a6e774d46bf",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/model_config.json",
"a36c185bfc17a83b6cfef6f98b29c909",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-base-en": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en_v1.1/model_state.pdparams",
"2baf0647774d6309e4b2be726ad4283a",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/model_config.json",
"2ca9fe0eea8ff9418725d1a24fcf5c36",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/vocab.txt",
"64800d5d8528ce344256daf115d4965e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-m-base": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base_v1.0/model_state.pdparams",
"ed96cb17b4b3283a65ad0846ada7799e",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/model_config.json",
"05c4b9d050e1402a891b207e36d2e501",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/vocab.txt",
"e6e1091c984592e72c4460e8eb25045e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/tokenizer_config.json",
"f144bd065ea90cc26eaa91197124bdcc",
],
"sentencepiece_model_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/sentencepiece.bpe.model",
"bf25eb5120ad92ef5c7d8596b5dc4046",
],
},
"uie-m-large": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large_v1.0/model_state.pdparams",
"75f3989c515f05f6842e314d3f75ee27",
],
"model_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/model_config.json",
"22ad69618dc3f4c3fe756e3044c3056e",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/vocab.txt",
"e6e1091c984592e72c4460e8eb25045e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/tokenizer_config.json",
"f144bd065ea90cc26eaa91197124bdcc",
],
"sentencepiece_model_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/sentencepiece.bpe.model",
"bf25eb5120ad92ef5c7d8596b5dc4046",
],
},
}
def __init__(self, task, model, schema, schema_lang="zh", **kwargs):
super().__init__(task=task, model=model, **kwargs)
if model in ["uie-m-base", "uie-m-large"]:
self._multilingual = True
self.resource_files_names["sentencepiece_model_file"] = "sentencepiece.bpe.model"
else:
self._multilingual = False
if "sentencepiece_model_file" in self.resource_files_names.keys():
del self.resource_files_names["sentencepiece_model_file"]
self._schema_tree = None
self.set_schema(schema)
self._check_task_files()
self._check_predictor_type()
self._get_inference_model()
self._usage = usage
self._is_en = True if model in ["uie-base-en"] or schema_lang == "en" else False
self._max_seq_len = self.kwargs["max_seq_len"] if "max_seq_len" in self.kwargs else 512
self._batch_size = self.kwargs["batch_size"] if "batch_size" in self.kwargs else 64
self._split_sentence = self.kwargs["split_sentence"] if "split_sentence" in self.kwargs else False
self._position_prob = self.kwargs["position_prob"] if "position_prob" in self.kwargs else 0.5
self._lazy_load = self.kwargs["lazy_load"] if "lazy_load" in self.kwargs else False
self._num_workers = self.kwargs["num_workers"] if "num_workers" in self.kwargs else 0
self.use_fast = self.kwargs["use_fast"] if "use_fast" in self.kwargs else False
self._construct_tokenizer()
def set_schema(self, schema):
if isinstance(schema, dict) or isinstance(schema, str):
schema = [schema]
self._schema_tree = self._build_tree(schema)
def _construct_input_spec(self):
"""
Construct the input spec for the convert dygraph model to static model.
"""
if self._multilingual:
self._input_spec = [
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="pos_ids"),
]
else:
self._input_spec = [
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="token_type_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="pos_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="att_mask"),
]
def _construct_model(self, model):
"""
Construct the inference model for the predictor.
"""
if self._multilingual:
model_instance = UIEM.from_pretrained(self._task_path)
else:
model_instance = UIE.from_pretrained(self._task_path)
self._model = model_instance
self._model.eval()
def _construct_tokenizer(self):
"""
Construct the tokenizer for the predictor.
"""
self._tokenizer = AutoTokenizer.from_pretrained(self._task_path, use_fast=self.use_fast)
def _preprocess(self, inputs):
"""
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)
outputs = {}
outputs["text"] = inputs
return outputs
def _single_stage_predict(self, inputs):
input_texts = []
prompts = []
for i in range(len(inputs)):
input_texts.append(inputs[i]["text"])
prompts.append(inputs[i]["prompt"])
# max predict length should exclude the length of prompt and summary tokens
max_predict_len = self._max_seq_len - len(max(prompts)) - 3
short_input_texts, self.input_mapping = self._auto_splitter(
input_texts, max_predict_len, split_sentence=self._split_sentence
)
short_texts_prompts = []
for k, v in self.input_mapping.items():
short_texts_prompts.extend([prompts[k] for i in range(len(v))])
short_inputs = [
{"text": short_input_texts[i], "prompt": short_texts_prompts[i]} for i in range(len(short_input_texts))
]
def read(inputs):
for example in inputs:
encoded_inputs = self._tokenizer(
text=[example["prompt"]],
text_pair=[example["text"]],
truncation=True,
max_seq_len=self._max_seq_len,
pad_to_max_seq_len=True,
return_attention_mask=True,
return_position_ids=True,
return_offsets_mapping=True,
)
if self._multilingual:
tokenized_output = [
encoded_inputs["input_ids"][0],
encoded_inputs["position_ids"][0],
encoded_inputs["offset_mapping"][0],
]
else:
tokenized_output = [
encoded_inputs["input_ids"][0],
encoded_inputs["token_type_ids"][0],
encoded_inputs["position_ids"][0],
encoded_inputs["attention_mask"][0],
encoded_inputs["offset_mapping"][0],
]
tokenized_output = [np.array(x, dtype="int64") for x in tokenized_output]
yield tuple(tokenized_output)
infer_ds = load_dataset(read, inputs=short_inputs, lazy=self._lazy_load)
batch_sampler = paddle.io.BatchSampler(dataset=infer_ds, batch_size=self._batch_size, shuffle=False)
infer_data_loader = paddle.io.DataLoader(
dataset=infer_ds, batch_sampler=batch_sampler, num_workers=self._num_workers, return_list=True
)
sentence_ids = []
probs = []
for batch in infer_data_loader:
if self._multilingual:
input_ids, pos_ids, offset_maps = batch
else:
input_ids, token_type_ids, pos_ids, att_mask, offset_maps = batch
if self._predictor_type == "paddle-inference":
if self._multilingual:
self.input_handles[0].copy_from_cpu(input_ids.numpy())
self.input_handles[1].copy_from_cpu(pos_ids.numpy())
else:
self.input_handles[0].copy_from_cpu(input_ids.numpy())
self.input_handles[1].copy_from_cpu(token_type_ids.numpy())
self.input_handles[2].copy_from_cpu(pos_ids.numpy())
self.input_handles[3].copy_from_cpu(att_mask.numpy())
self.predictor.run()
start_prob = self.output_handle[0].copy_to_cpu().tolist()
end_prob = self.output_handle[1].copy_to_cpu().tolist()
else:
if self._multilingual:
input_dict = {
"input_ids": input_ids.numpy(),
"pos_ids": pos_ids.numpy(),
}
else:
input_dict = {
"input_ids": input_ids.numpy(),
"token_type_ids": token_type_ids.numpy(),
"pos_ids": pos_ids.numpy(),
"att_mask": att_mask.numpy(),
}
start_prob, end_prob = self.predictor.run(None, input_dict)
start_prob = start_prob.tolist()
end_prob = end_prob.tolist()
start_ids_list = get_bool_ids_greater_than(start_prob, limit=self._position_prob, return_prob=True)
end_ids_list = get_bool_ids_greater_than(end_prob, limit=self._position_prob, return_prob=True)
for start_ids, end_ids, offset_map in zip(start_ids_list, end_ids_list, offset_maps.tolist()):
span_set = get_span(start_ids, end_ids, with_prob=True)
sentence_id, prob = get_id_and_prob(span_set, offset_map)
sentence_ids.append(sentence_id)
probs.append(prob)
results = self._convert_ids_to_results(short_inputs, sentence_ids, probs)
results = self._auto_joiner(results, short_input_texts, self.input_mapping)
return results
def _auto_joiner(self, short_results, short_inputs, input_mapping):
concat_results = []
is_cls_task = False
for short_result in short_results:
if short_result == []:
continue
elif "start" not in short_result[0].keys() and "end" not in short_result[0].keys():
is_cls_task = True
break
else:
break
for k, vs in input_mapping.items():
if is_cls_task:
cls_options = {}
single_results = []
for v in vs:
if len(short_results[v]) == 0:
continue
if short_results[v][0]["text"] not in cls_options.keys():
cls_options[short_results[v][0]["text"]] = [1, short_results[v][0]["probability"]]
else:
cls_options[short_results[v][0]["text"]][0] += 1
cls_options[short_results[v][0]["text"]][1] += short_results[v][0]["probability"]
if len(cls_options) != 0:
cls_res, cls_info = max(cls_options.items(), key=lambda x: x[1])
concat_results.append([{"text": cls_res, "probability": cls_info[1] / cls_info[0]}])
else:
concat_results.append([])
else:
offset = 0
single_results = []
for v in vs:
if v == 0:
single_results = short_results[v]
offset += len(short_inputs[v])
else:
for i in range(len(short_results[v])):
if "start" not in short_results[v][i] or "end" not in short_results[v][i]:
continue
short_results[v][i]["start"] += offset
short_results[v][i]["end"] += offset
offset += len(short_inputs[v])
single_results.extend(short_results[v])
concat_results.append(single_results)
return concat_results
def _run_model(self, inputs):
raw_inputs = inputs["text"]
results = self._multi_stage_predict(raw_inputs)
inputs["result"] = results
return inputs
def _multi_stage_predict(self, data):
"""
Traversal the schema tree and do multi-stage prediction.
Args:
data (list): a list of strings
Returns:
list: a list of predictions, where the list's length
equals to the length of `data`
"""
results = [{} for _ in range(len(data))]
# input check to early return
if len(data) < 1 or self._schema_tree is None:
return results
# copy to stay `self._schema_tree` unchanged
schema_list = self._schema_tree.children[:]
while len(schema_list) > 0:
node = schema_list.pop(0)
examples = []
input_map = {}
cnt = 0
idx = 0
if not node.prefix:
for one_data in data:
examples.append({"text": one_data, "prompt": dbc2sbc(node.name)})
input_map[cnt] = [idx]
idx += 1
cnt += 1
else:
for pre, one_data in zip(node.prefix, data):
if len(pre) == 0:
input_map[cnt] = []
else:
for p in pre:
if self._is_en:
if re.search(r"\[.*?\]$", node.name):
prompt_prefix = node.name[: node.name.find("[", 1)].strip()
cls_options = re.search(r"\[.*?\]$", node.name).group()
# Sentiment classification of xxx [positive, negative]
prompt = prompt_prefix + p + " " + cls_options
else:
prompt = node.name + p
else:
prompt = p + node.name
examples.append({"text": one_data, "prompt": dbc2sbc(prompt)})
input_map[cnt] = [i + idx for i in range(len(pre))]
idx += len(pre)
cnt += 1
if len(examples) == 0:
result_list = []
else:
result_list = self._single_stage_predict(examples)
if not node.parent_relations:
relations = [[] for i in range(len(data))]
for k, v in input_map.items():
for idx in v:
if len(result_list[idx]) == 0:
continue
if node.name not in results[k].keys():
results[k][node.name] = result_list[idx]
else:
results[k][node.name].extend(result_list[idx])
if node.name in results[k].keys():
relations[k].extend(results[k][node.name])
else:
relations = node.parent_relations
for k, v in input_map.items():
for i in range(len(v)):
if len(result_list[v[i]]) == 0:
continue
if "relations" not in relations[k][i].keys():
relations[k][i]["relations"] = {node.name: result_list[v[i]]}
elif node.name not in relations[k][i]["relations"].keys():
relations[k][i]["relations"][node.name] = result_list[v[i]]
else:
relations[k][i]["relations"][node.name].extend(result_list[v[i]])
new_relations = [[] for i in range(len(data))]
for i in range(len(relations)):
for j in range(len(relations[i])):
if "relations" in relations[i][j].keys() and node.name in relations[i][j]["relations"].keys():
for k in range(len(relations[i][j]["relations"][node.name])):
new_relations[i].append(relations[i][j]["relations"][node.name][k])
relations = new_relations
prefix = [[] for _ in range(len(data))]
for k, v in input_map.items():
for idx in v:
for i in range(len(result_list[idx])):
if self._is_en:
prefix[k].append(" of " + result_list[idx][i]["text"])
else:
prefix[k].append(result_list[idx][i]["text"] + "的")
for child in node.children:
child.prefix = prefix
child.parent_relations = relations
schema_list.append(child)
return results
def _convert_ids_to_results(self, examples, sentence_ids, probs):
"""
Convert ids to raw text in a single stage.
"""
results = []
for example, sentence_id, prob in zip(examples, sentence_ids, probs):
if len(sentence_id) == 0:
results.append([])
continue
result_list = []
text = example["text"]
prompt = example["prompt"]
for i in range(len(sentence_id)):
start, end = sentence_id[i]
if start < 0 and end >= 0:
continue
if end < 0:
start += len(prompt) + 1
end += len(prompt) + 1
result = {"text": prompt[start:end], "probability": prob[i]}
result_list.append(result)
else:
result = {"text": text[start:end], "start": start, "end": end, "probability": prob[i]}
result_list.append(result)
results.append(result_list)
return results
@classmethod
def _build_tree(cls, schema, name="root"):
"""
Build the schema tree.
"""
schema_tree = SchemaTree(name)
for s in schema:
if isinstance(s, str):
schema_tree.add_child(SchemaTree(s))
elif isinstance(s, dict):
for k, v in s.items():
if isinstance(v, str):
child = [v]
elif isinstance(v, list):
child = v
else:
raise TypeError(
"Invalid schema, value for each key:value pairs should be list or string"
"but {} received".format(type(v))
)
schema_tree.add_child(cls._build_tree(child, name=k))
else:
raise TypeError("Invalid schema, element should be string or dict, " "but {} received".format(type(s)))
return schema_tree
def _postprocess(self, inputs):
"""
This function will convert the model output to raw text.
"""
return inputs["result"]
[文档]class GPTask(Task):
"""
Global Pointer for closed-domain information extraction Task.
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",
"vocab_file": "vocab.txt",
"special_tokens_map": "special_tokens_map.json",
"tokenizer_config": "tokenizer_config.json",
}
def __init__(self, task, model, **kwargs):
super().__init__(task=task, model=model, **kwargs)
self._schema_tree = None
self._load_config()
self._construct_tokenizer()
self._get_inference_model()
self._max_seq_len = self.kwargs["max_seq_len"] if "max_seq_len" in self.kwargs else 256
self._batch_size = self.kwargs["batch_size"] if "batch_size" in self.kwargs else 64
self._lazy_load = self.kwargs["lazy_load"] if "lazy_load" in self.kwargs else False
self._num_workers = self.kwargs["num_workers"] if "num_workers" in self.kwargs else 0
def _load_config(self):
model_config_file = os.path.join(self._task_path, self.resource_files_names["model_config"])
with open(model_config_file, encoding="utf-8") as f:
model_config = json.load(f)
self._label_maps = model_config["label_maps"]
self._task_type = model_config["task_type"]
self._encoder = model_config["encoder"]
schema = model_config["label_maps"]["schema"]
self._set_schema(schema)
def _set_schema(self, schema):
if isinstance(schema, dict) or isinstance(schema, str):
schema = [schema]
self._schema_tree = self._build_tree(schema)
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="input_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="att_mask"),
]
def _construct_model(self, model):
"""
Construct the inference model for the predictor.
"""
encoder = AutoModel.from_pretrained(self._encoder)
if self._task_type == "entity_extraction":
model_instance = GlobalPointerForEntityExtraction(encoder, self._label_maps)
else:
model_instance = GPLinkerForRelationExtraction(encoder, self._label_maps)
model_path = os.path.join(self._task_path, "model_state.pdparams")
state_dict = paddle.load(model_path)
model_instance.set_dict(state_dict)
self._model = model_instance
self._model.eval()
def _construct_tokenizer(self):
"""
Construct the tokenizer for the predictor.
"""
# TODO(zhoushunjie): Will set use_fast=True in future.
self._tokenizer = AutoTokenizer.from_pretrained(self._task_path)
def _preprocess(self, inputs):
"""
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)
def read(inputs):
for x in inputs:
tokenized_inputs = self._tokenizer(
x,
max_length=self._max_seq_len,
padding=False,
truncation=True,
return_attention_mask=True,
return_offsets_mapping=True,
return_token_type_ids=False,
)
tokenized_inputs["text"] = x
yield tokenized_inputs
infer_ds = load_dataset(read, inputs=inputs, lazy=self._lazy_load)
data_collator = DataCollatorGP(self._tokenizer, label_maps=self._label_maps, task_type=self._task_type)
batch_sampler = paddle.io.BatchSampler(dataset=infer_ds, batch_size=self._batch_size, shuffle=False)
infer_data_loader = paddle.io.DataLoader(
dataset=infer_ds,
batch_sampler=batch_sampler,
collate_fn=data_collator,
num_workers=self._num_workers,
return_list=True,
)
outputs = {}
outputs["data_loader"] = infer_data_loader
outputs["input_texts"] = inputs
return outputs
def _run_model(self, inputs):
all_preds = ([], []) if self._task_type in ["opinion_extraction", "relation_extraction"] else []
for batch in inputs["data_loader"]:
input_ids, attention_masks, offset_mappings, texts = batch
self.input_handles[0].copy_from_cpu(input_ids.numpy().astype("int64"))
self.input_handles[1].copy_from_cpu(attention_masks.numpy().astype("int64"))
self.predictor.run()
logits = [paddle.to_tensor(self.output_handle[i].copy_to_cpu()) for i in range(len(self.output_handle))]
batch_outputs = gp_decode(logits, offset_mappings, texts, self._label_maps, self._task_type)
if isinstance(batch_outputs, tuple):
all_preds[0].extend(batch_outputs[0]) # Entity output
all_preds[1].extend(batch_outputs[1]) # Relation output
else:
all_preds.extend(batch_outputs)
inputs["result"] = all_preds
return inputs
@classmethod
def _build_tree(cls, schema, name="root"):
"""
Build the schema tree.
"""
schema_tree = SchemaTree(name)
for s in schema:
if isinstance(s, str):
schema_tree.add_child(SchemaTree(s))
elif isinstance(s, dict):
for k, v in s.items():
if isinstance(v, str):
child = [v]
elif isinstance(v, list):
child = v
else:
raise TypeError(
"Invalid schema, value for each key:value pairs should be list or string"
"but {} received".format(type(v))
)
schema_tree.add_child(cls._build_tree(child, name=k))
else:
raise TypeError("Invalid schema, element should be string or dict, " "but {} received".format(type(s)))
return schema_tree
def _postprocess(self, inputs):
if self._task_type == "entity_extraction":
results = self._postprocess_entity_extraction(inputs)
elif self._task_type == "opinion_extraction":
results = self._postprocess_opinion_extraction(inputs)
else:
results = self._postprocess_relation_extraction(inputs)
return results
def _postprocess_opinion_extraction(self, inputs):
all_ent_preds, all_rel_preds = inputs["result"]
results = []
for i in range(len(inputs["input_texts"])):
result = {}
aspect_maps = {}
for ent in all_ent_preds[i]:
ent_res = {
"text": ent["text"],
"start": ent["start_index"],
"end": ent["start_index"] + len(ent["text"]),
"probability": ent["probability"],
}
result.setdefault(ent["type"], []).append(ent_res)
if ent["type"] == "评价维度":
for r in result["评价维度"]:
if ent["text"] == r["text"] and ent["start_index"] == r["start"]:
aspect_maps[(ent["text"], ent["start_index"])] = r
break
for rel in all_rel_preds[i]:
r = aspect_maps[(rel["aspect"], rel["aspect_start_index"])]
r["relations"] = {}
sentiment = {"probability": rel["probability"], "text": rel["sentiment"]}
opinion = {
"text": rel["opinion"],
"start": rel["opinion_start_index"],
"end": rel["opinion_start_index"] + len(rel["opinion"]),
"probability": rel["probability"],
}
r["relations"].setdefault("情感倾向[正向，负向]", []).append(sentiment)
r["relations"].setdefault("观点词", []).append(opinion)
results.append(result)
return results
def _postprocess_relation_extraction(self, inputs):
all_ent_preds, all_rel_preds = inputs["result"]
results = []
for input_text_idx in range(len(inputs["input_texts"])):
result = {}
schema_list = self._schema_tree.children[:]
while len(schema_list) > 0:
node = schema_list.pop(0)
if node.parent_relations is None:
prefix = []
relations = [[]]
cnt = -1
for ent in all_ent_preds[input_text_idx]:
if node.name == ent["type"]:
ent_res = {
"text": ent["text"],
"start": ent["start_index"],
"end": ent["start_index"] + len(ent["text"]),
"probability": ent["probability"],
}
result.setdefault(node.name, []).append(ent_res)
cnt += 1
result[node.name][cnt]["relations"] = {}
relations[0].append(result[node.name][cnt])
else:
relations = [[] for _ in range(len(node.parent_relations))]
for i, rs in enumerate(node.parent_relations):
for r in rs:
cnt = -1
for rel in all_rel_preds[input_text_idx]:
if (
r["text"] == rel["subject"]
and r["start"] == rel["subject_start_index"]
and node.name == rel["predicate"]
):
rel_res = {
"text": rel["object"],
"start": rel["object_start_index"],
"end": rel["object_start_index"] + len(rel["object"]),
"probability": rel["probability"],
}
r["relations"].setdefault(node.name, []).append(rel_res)
cnt += 1
r["relations"][node.name][cnt]["relations"] = {}
relations[i].append(r["relations"][node.name][cnt])
for child in node.children:
child.prefix = prefix
child.parent_relations = relations
schema_list.append(child)
results.append(result)
return results
def _postprocess_entity_extraction(self, inputs):
all_preds = inputs["result"]
results = []
for input_text_idx in range(len(inputs["input_texts"])):
result = {}
schema_list = self._schema_tree.children[:]
while len(schema_list) > 0:
node = schema_list.pop(0)
for ent in all_preds[input_text_idx]:
if node.name == ent["type"]:
ent_res = {
"text": ent["text"],
"start": ent["start_index"],
"end": ent["start_index"] + len(ent["text"]),
"probability": ent["probability"],
}
result.setdefault(node.name, []).append(ent_res)
results.append(result)
return results