paddlenlp.taskflow.information_extraction 源代码

# coding:utf-8
# Copyright (c) 2022  PaddlePaddle Authors. All Rights Reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import re
import numpy as np
import paddle
from ..datasets import load_dataset
from ..transformers import AutoTokenizer
from .models import UIE
from .task import Task
from .utils import SchemaTree, get_span, get_id_and_prob, get_bool_ids_greater_than, dbc2sbc

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}]}]
            '''

            schema = [{'Comment object': ['Opinion', 'Sentiment classification [negative, positive]']}]
            ie_en.set_schema(schema)
            ie_en("overall i 'm happy with my toy.")
            '''
            
            '''
         """


[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_v1.0/model_state.pdparams", "d12e03c2bfe2824c876883b4b836d79d" ], "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" ] }, } def __init__(self, task, model, schema, **kwargs): super().__init__(task=task, model=model, **kwargs) self._schema_tree = None self.set_schema(schema) self._check_task_files() self._construct_tokenizer() self._check_predictor_type() self._get_inference_model() self._usage = usage self._is_en = False if model not in [ "uie-base-en", ] else True 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 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='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. """ 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) 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_dict=False, return_offsets_mapping=True) encoded_inputs = encoded_inputs[0] tokenized_output = [ encoded_inputs["input_ids"], encoded_inputs["token_type_ids"], encoded_inputs["position_ids"], encoded_inputs["attention_mask"], encoded_inputs["offset_mapping"] ] 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: input_ids, token_type_ids, pos_ids, att_mask, offset_maps = batch if self._predictor_type == "paddle-inference": 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: 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, ids, offset_map in zip( start_ids_list, end_ids_list, input_ids.tolist(), offset_maps.tolist()): for i in reversed(range(len(ids))): if ids[i] != 0: ids = ids[:i] break 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']