data_process.py

# Copyright (c) 2021 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 paddle


def load_dict(dict_path):
    vocab = {}
    i = 0
    with open(dict_path, "r", encoding="utf-8") as fin:
        for line in fin:
            vocab[line.strip()] = i
            i += 1
    return vocab


def convert_example(example, tokenizer, max_seq_len, tags_to_idx=None, summary_num=2, is_test=False):
    tokens = example["tokens"]
    tokenized_input = tokenizer(tokens, return_length=True, is_split_into_words="token", max_seq_len=max_seq_len)

    if is_test:
        return tokenized_input["input_ids"], tokenized_input["token_type_ids"], tokenized_input["seq_len"]

    tags = example["tags"]
    if len(tokenized_input["input_ids"]) - 1 - summary_num < len(tags):
        tags = tags[: len(tokenized_input["input_ids"]) - 1 - summary_num]
    # '[CLS]' and '[SEP]' will get label 'O'
    tags = ["O"] * (summary_num) + tags + ["O"]
    tags += ["O"] * (len(tokenized_input["input_ids"]) - len(tags))
    tokenized_input["tags"] = [tags_to_idx[x] for x in tags]
    return (
        tokenized_input["input_ids"],
        tokenized_input["token_type_ids"],
        tokenized_input["seq_len"],
        tokenized_input["tags"],
    )


def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == "train" else False
    if mode == "train":
        batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
    else:
        batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)

    return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True)


def read_custom_data(filename):
    """Reads data"""
    with open(filename, "r", encoding="utf-8") as f:
        for line in f:
            example = transfer_str_to_example(line.strip())
            yield example


def transfer_str_to_example(sample):
    text = ""
    tags = []
    items = sample.split(" ")
    items = [item.rsplit("/", 1) for item in items]
    for w, t in items:
        text += w
        if len(w) == 1:
            tags.append(f"S-{t}")
        else:
            l = len(w)
            for j in range(l):
                if j == 0:
                    tags.append(f"B-{t}")
                elif j == l - 1:
                    tags.append(f"E-{t}")
                else:
                    tags.append(f"I-{t}")
    res = {
        "tokens": list(text),
        "tags": tags,
    }
    return res