wehub-resource-sync
94 lines
3.7 KiB
Python
94 lines
3.7 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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 numpy as np
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import paddle
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def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
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"""
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Create dataloader.
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Args:
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dataset(obj:`paddle.io.Dataset`): Dataset instance.
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mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
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batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
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batchify_fn(obj:`callable`, optional, defaults to `None`): function to generate mini-batch data by merging
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the sample list, None for only stack each fields of sample in axis
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0(same as :attr::`np.stack(..., axis=0)`).
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trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
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Returns:
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dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
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"""
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if trans_fn:
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dataset = dataset.map(trans_fn)
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shuffle = True if mode == "train" else False
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if mode == "train":
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sampler = paddle.io.DistributedBatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
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else:
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sampler = paddle.io.BatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
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dataloader = paddle.io.DataLoader(dataset, batch_sampler=sampler, collate_fn=batchify_fn)
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return dataloader
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def preprocess_prediction_data(data, tokenizer, pad_token_id=0, max_ngram_filter_size=3):
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"""
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It process the prediction data as the format used as training.
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Args:
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data (obj:`list[str]`): The prediction data whose each element is a tokenized text.
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tokenizer(obj: paddlenlp.data.JiebaTokenizer): It use jieba to cut the chinese string.
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pad_token_id(obj:`int`, optional, defaults to 0): The pad token index.
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max_ngram_filter_size (obj:`int`, optional, defaults to 3) Max n-gram size in TextCNN model.
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Users should refer to the ngram_filter_sizes setting in TextCNN, if ngram_filter_sizes=(1, 2, 3)
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then max_ngram_filter_size=3
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Returns:
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examples (obj:`list`): The processed data whose each element
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is a `list` object, which contains
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- word_ids(obj:`list[int]`): The list of word ids.
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"""
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examples = []
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for text in data:
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ids = tokenizer.encode(text)
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seq_len = len(ids)
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# Sequence length should larger or equal than the maximum ngram_filter_size in TextCNN model
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if seq_len < max_ngram_filter_size:
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ids.extend([pad_token_id] * (max_ngram_filter_size - seq_len))
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examples.append(ids)
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return examples
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def convert_example(example, tokenizer):
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"""convert_example"""
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input_ids = tokenizer.encode(example["text"])
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input_ids = np.array(input_ids, dtype="int64")
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label = np.array(example["label"], dtype="int64")
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return input_ids, label
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def read_custom_data(filename):
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"""Reads data."""
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with open(filename, "r", encoding="utf-8") as f:
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# Skip head
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next(f)
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for line in f:
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data = line.strip().split("\t")
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label, text = data
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yield {"text": text, "label": label}
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