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- # 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 os
- import time
- import argparse
- import json
- from decimal import Decimal
- import numpy as np
- from paddlenlp.utils.log import logger
-
- from utils import set_seed, convert_ext_examples, convert_cls_examples
-
-
- def do_convert():
- set_seed(args.seed)
-
- tic_time = time.time()
- if not os.path.exists(args.doccano_file):
- raise ValueError("Please input the correct path of doccano file.")
-
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- if len(args.splits) != 0 and len(args.splits) != 3:
- raise ValueError("Only []/ len(splits)==3 accepted for splits.")
-
- def _check_sum(splits):
- return Decimal(str(splits[0])) + Decimal(str(splits[1])) + Decimal(
- str(splits[2])) == Decimal("1")
-
- if len(args.splits) == 3 and not _check_sum(args.splits):
- raise ValueError(
- "Please set correct splits, sum of elements in splits should be equal to 1."
- )
-
- with open(args.doccano_file, "r", encoding="utf-8") as f:
- raw_examples = f.readlines()
-
- def _create_ext_examples(examples,
- negative_ratio,
- prompt_prefix="情感倾向",
- options=["正向", "负向"],
- separator="##",
- shuffle=False,
- is_train=True):
- entities, relations, aspects = convert_ext_examples(
- examples, negative_ratio, prompt_prefix, options, separator,
- is_train)
- examples = entities + relations + aspects
- if shuffle:
- indexes = np.random.permutation(len(examples))
- examples = [examples[i] for i in indexes]
- return examples
-
- def _create_cls_examples(examples, prompt_prefix, options, shuffle=False):
- examples = convert_cls_examples(examples, prompt_prefix, options)
- if shuffle:
- indexes = np.random.permutation(len(examples))
- examples = [examples[i] for i in indexes]
- return examples
-
- def _save_examples(save_dir, file_name, examples):
- count = 0
- save_path = os.path.join(save_dir, file_name)
- with open(save_path, "w", encoding="utf-8") as f:
- for example in examples:
- f.write(json.dumps(example, ensure_ascii=False) + "\n")
- count += 1
- logger.info("Save %d examples to %s." % (count, save_path))
-
- if len(args.splits) == 0:
- if args.task_type == "ext":
- examples = _create_ext_examples(raw_examples, args.negative_ratio,
- args.prompt_prefix, args.options,
- args.separator, args.is_shuffle)
- else:
- examples = _create_cls_examples(raw_examples, args.prompt_prefix,
- args.options, args.is_shuffle)
- _save_examples(args.save_dir, "train.txt", examples)
- else:
- if args.is_shuffle:
- indexes = np.random.permutation(len(raw_examples))
- index_list = indexes.tolist()
- raw_examples = [raw_examples[i] for i in indexes]
-
- i1, i2, _ = args.splits
- p1 = int(len(raw_examples) * i1)
- p2 = int(len(raw_examples) * (i1 + i2))
-
- train_ids = index_list[:p1]
- dev_ids = index_list[p1:p2]
- test_ids = index_list[p2:]
-
- with open(os.path.join(args.save_dir, "sample_index.json"), "w") as fp:
- maps = {
- "train_ids": train_ids,
- "dev_ids": dev_ids,
- "test_ids": test_ids
- }
- fp.write(json.dumps(maps))
-
- if args.task_type == "ext":
- train_examples = _create_ext_examples(raw_examples[:p1],
- args.negative_ratio,
- args.prompt_prefix,
- args.options, args.separator,
- args.is_shuffle)
- dev_examples = _create_ext_examples(raw_examples[p1:p2],
- -1,
- args.prompt_prefix,
- args.options,
- args.separator,
- is_train=False)
- test_examples = _create_ext_examples(raw_examples[p2:],
- -1,
- args.prompt_prefix,
- args.options,
- args.separator,
- is_train=False)
- else:
- train_examples = _create_cls_examples(raw_examples[:p1],
- args.prompt_prefix,
- args.options)
- dev_examples = _create_cls_examples(raw_examples[p1:p2],
- args.prompt_prefix,
- args.options)
- test_examples = _create_cls_examples(raw_examples[p2:],
- args.prompt_prefix,
- args.options)
-
- _save_examples(args.save_dir, "train.txt", train_examples)
- _save_examples(args.save_dir, "dev.txt", dev_examples)
- _save_examples(args.save_dir, "test.txt", test_examples)
-
- logger.info('Finished! It takes %.2f seconds' % (time.time() - tic_time))
-
-
- if __name__ == "__main__":
- # yapf: disable
- parser = argparse.ArgumentParser()
-
- parser.add_argument("--doccano_file", default="./data/doccano.json", type=str, help="The doccano file exported from doccano platform.")
- parser.add_argument("--save_dir", default="./data", type=str, help="The path of data that you wanna save.")
- parser.add_argument("--negative_ratio", default=5, type=int, help="Used only for the extraction task, the ratio of positive and negative samples, number of negtive samples = negative_ratio * number of positive samples")
- parser.add_argument("--splits", default=[0.8, 0.1, 0.1], type=float, nargs="*", help="The ratio of samples in datasets. [0.6, 0.2, 0.2] means 60% samples used for training, 20% for evaluation and 20% for test.")
- parser.add_argument("--task_type", choices=['ext', 'cls'], default="ext", type=str, help="Select task type, ext for the extraction task and cls for the classification task, defaults to ext.")
- parser.add_argument("--options", default=["正向", "负向"], type=str, nargs="+", help="Used only for the classification task, the options for classification")
- parser.add_argument("--prompt_prefix", default="情感倾向", type=str, help="Used only for the classification task, the prompt prefix for classification")
- parser.add_argument("--is_shuffle", default=True, type=bool, help="Whether to shuffle the labeled dataset, defaults to True.")
- parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization")
- parser.add_argument("--separator", type=str, default='##', help="Used only for entity/aspect-level classification task, separator for entity label and classification label")
-
- args = parser.parse_args()
- # yapf: enable
-
- do_convert()
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