wehub-resource-sync
139 lines
4.5 KiB
Python
139 lines
4.5 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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from tqdm import tqdm
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ENTITY_TOKEN = "[ENTITY]"
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class InputExample(object):
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def __init__(self, id_, text, span, labels):
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self.id = id_
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self.text = text
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self.span = span
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self.labels = labels
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class InputFeatures(object):
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def __init__(
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self,
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word_ids,
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word_segment_ids,
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word_attention_mask,
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entity_ids,
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entity_position_ids,
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entity_segment_ids,
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entity_attention_mask,
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labels,
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):
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self.word_ids = word_ids
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self.word_segment_ids = word_segment_ids
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self.word_attention_mask = word_attention_mask
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self.entity_ids = entity_ids
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self.entity_position_ids = entity_position_ids
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self.entity_segment_ids = entity_segment_ids
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self.entity_attention_mask = entity_attention_mask
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self.labels = labels
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class DatasetProcessor(object):
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def get_train_examples(self, data_dir):
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return self._create_examples(data_dir, "train")
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def get_dev_examples(self, data_dir):
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return self._create_examples(data_dir, "dev")
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def get_test_examples(self, data_dir):
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return self._create_examples(data_dir, "test")
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def get_label_list(self, data_dir):
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labels = set()
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for example in self.get_train_examples(data_dir):
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labels.update(example.labels)
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return sorted(labels)
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def _create_examples(self, data_dir, set_type):
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with open(os.path.join(data_dir, set_type + ".json"), "r") as f:
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data = json.load(f)
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return [
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InputExample(i, item["sent"], (item["start"], item["end"]), item["labels"]) for i, item in enumerate(data)
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]
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def convert_examples_to_features(examples, label_list, tokenizer, max_mention_length):
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label_map = {label: i for i, label in enumerate(label_list)}
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conv_tables = (
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("-LRB-", "("),
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("-LCB-", "("),
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("-LSB-", "("),
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("-RRB-", ")"),
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("-RCB-", ")"),
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("-RSB-", ")"),
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)
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features = []
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for example in tqdm(examples):
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def preprocess_and_tokenize(text, start, end=None):
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target_text = text[start:end].rstrip()
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for a, b in conv_tables:
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target_text = target_text.replace(a, b)
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return tokenizer.tokenize(target_text, add_prefix_space=True)
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tokens = [tokenizer.cls_token]
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tokens += preprocess_and_tokenize(example.text, 0, example.span[0])
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mention_start = len(tokens)
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tokens.append(ENTITY_TOKEN)
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tokens += preprocess_and_tokenize(example.text, example.span[0], example.span[1])
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tokens.append(ENTITY_TOKEN)
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mention_end = len(tokens)
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tokens += preprocess_and_tokenize(example.text, example.span[1])
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tokens.append(tokenizer.sep_token)
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word_ids = tokenizer.convert_tokens_to_ids(tokens)
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word_attention_mask = [1] * len(tokens)
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word_segment_ids = [0] * len(tokens)
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entity_ids = [2, 0]
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entity_attention_mask = [1, 0]
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entity_segment_ids = [0, 0]
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entity_position_ids = list(range(mention_start, mention_end))[:max_mention_length]
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entity_position_ids += [-1] * (max_mention_length - mention_end + mention_start)
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entity_position_ids = [entity_position_ids, [-1] * max_mention_length]
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labels = [0] * len(label_map)
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for label in example.labels:
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labels[label_map[label]] = 1
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features.append(
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InputFeatures(
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word_ids=word_ids,
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word_segment_ids=word_segment_ids,
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word_attention_mask=word_attention_mask,
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entity_ids=entity_ids,
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entity_position_ids=entity_position_ids,
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entity_segment_ids=entity_segment_ids,
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entity_attention_mask=entity_attention_mask,
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labels=labels,
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)
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)
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return features
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