data.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
import numpy as np

from paddlenlp.datasets import MapDataset


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_text_pair(data_path, is_test=False):
    """Reads data."""
    with open(data_path, 'r', encoding='utf-8') as f:
        for line in f:
            data = line.rstrip().split("\t")
            if is_test == False:
                if len(data) != 3:
                    continue
                yield {'query1': data[0], 'query2': data[1], 'label': data[2]}
            else:
                if len(data) != 2:
                    continue
                yield {'query1': data[0], 'query2': data[1]}


def convert_example(example, tokenizer, max_seq_length=512, is_test=False):

    query, title = example["query1"], example["query2"]

    encoded_inputs = tokenizer(
        text=query, text_pair=title, max_seq_len=max_seq_length)

    input_ids = encoded_inputs["input_ids"]
    token_type_ids = encoded_inputs["token_type_ids"]

    if not is_test:
        label = np.array([example["label"]], dtype="int64")
        return input_ids, token_type_ids, label
    else:
        return input_ids, token_type_ids