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- # 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
- import numpy as np
-
- from utils import set_seed, convert_ext_examples
-
-
- def do_convert():
- set_seed(args.seed)
-
- tic_time = time.time()
- if not os.path.exists(args.input_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.")
-
- if args.splits and sum(args.splits) != 1:
- raise ValueError("Please set correct splits, sum of elements in splits should be equal to 1.")
-
- with open(args.input_file, "r", encoding="utf-8") as f:
- raw_examples = f.readlines()
-
- def _create_ext_examples(examples, negative_ratio=0, shuffle=False):
- entities, relations = convert_ext_examples(examples, negative_ratio)
- examples = [e + r for e, r in zip(entities, relations)]
- 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:
- for x in example:
- f.write(json.dumps(x, ensure_ascii=False) + "\n")
- count += 1
- print("\nSave %d examples to %s." % (count, save_path))
-
- if len(args.splits) == 0:
- examples = _create_ext_examples(raw_examples, args.negative_ratio, args.is_shuffle)
- _save_examples(args.save_dir, "train.txt", examples)
- else:
- if args.is_shuffle:
- indexes = np.random.permutation(len(raw_examples))
- 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_examples = _create_ext_examples(raw_examples[:p1], args.negative_ratio, args.is_shuffle)
- dev_examples = _create_ext_examples(raw_examples[p1:p2])
- test_examples = _create_ext_examples(raw_examples[p2:])
-
- _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)
-
- print("Finished! It takes %.2f seconds" % (time.time() - tic_time))
-
-
- if __name__ == "__main__":
- # yapf: disable
- parser = argparse.ArgumentParser()
-
- parser.add_argument("--input_file", default="./data/data.json", type=str, help="The data file exported from doccano platform.")
- parser.add_argument("--save_dir", default="./data", type=str, help="The path to save processed data.")
- parser.add_argument("--negative_ratio", default=5, type=int, help="Used only for the classification 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("--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")
-
- args = parser.parse_args()
- # yapf: enable
-
- do_convert()
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