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Add sample script of data processing for fine-tuning BERT on ClUE dataset
Add sample script of data processing for fine-tuning BERT on CLUE dataset fix pylint fix pylint missing-docstring Add sample script of data processing for fine-tuning BERT on CLUE dataset fix pylint fix pylint missing-docstring fix pylint
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dessyang
xiefangqi
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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sample script of processing CLUE classification dataset using mindspore.dataset.text for fine-tuning bert
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"""
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import os
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import numpy as np
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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import mindspore.dataset.text as text
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import mindspore.dataset.transforms.c_transforms as ops
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def process_tnews_clue_dataset(data_dir, label_list, bert_vocab_path,
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data_usage='train', shuffle_dataset=False, max_seq_len=128, batch_size=64):
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"""Process TNEWS dataset"""
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### Loading TNEWS from CLUEDataset
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assert data_usage in ['train', 'eval', 'test']
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if data_usage == 'train':
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dataset = ds.CLUEDataset(os.path.join(data_dir, "train.json"), task='TNEWS',
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usage=data_usage, shuffle=shuffle_dataset)
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elif data_usage == 'eval':
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dataset = ds.CLUEDataset(os.path.join(data_dir, "dev.json"), task='TNEWS',
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usage=data_usage, shuffle=shuffle_dataset)
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else:
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dataset = ds.CLUEDataset(os.path.join(data_dir, "test.json"), task='TNEWS',
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usage=data_usage, shuffle=shuffle_dataset)
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### Processing label
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if data_usage == 'test':
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dataset = dataset.map(input_columns=["id"], output_columns=["id", "label_id"],
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columns_order=["id", "label_id", "sentence"], operations=ops.Duplicate())
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dataset = dataset.map(input_columns=["label_id"], operations=ops.Fill(0))
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else:
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label_vocab = text.Vocab.from_list(label_list)
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label_lookup = text.Lookup(label_vocab)
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dataset = dataset.map(input_columns="label_desc", output_columns="label_id", operations=label_lookup)
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### Processing sentence
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vocab = text.Vocab.from_file(bert_vocab_path)
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tokenizer = text.BertTokenizer(vocab, lower_case=True)
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lookup = text.Lookup(vocab, unknown_token='[UNK]')
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dataset = dataset.map(input_columns=["sentence"], operations=tokenizer)
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dataset = dataset.map(input_columns=["sentence"], operations=ops.Slice(slice(0, max_seq_len)))
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dataset = dataset.map(input_columns=["sentence"],
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operations=ops.Concatenate(prepend=np.array(["[CLS]"], dtype='S'),
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append=np.array(["[SEP]"], dtype='S')))
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dataset = dataset.map(input_columns=["sentence"], output_columns=["text_ids"], operations=lookup)
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dataset = dataset.map(input_columns=["text_ids"], operations=ops.PadEnd([max_seq_len], 0))
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dataset = dataset.map(input_columns=["text_ids"], output_columns=["text_ids", "mask_ids"],
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columns_order=["label_id", "text_ids", "mask_ids"], operations=ops.Duplicate())
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dataset = dataset.map(input_columns=["mask_ids"], operations=ops.Mask(ops.Relational.NE, 0, mstype.int32))
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dataset = dataset.map(input_columns=["text_ids"], output_columns=["text_ids", "segment_ids"],
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columns_order=["label_id", "text_ids", "mask_ids", "segment_ids"], operations=ops.Duplicate())
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dataset = dataset.map(input_columns=["segment_ids"], operations=ops.Fill(0))
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dataset = dataset.batch(batch_size)
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label = []
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text_ids = []
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mask_ids = []
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segment_ids = []
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for data in dataset:
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label.append(data[0])
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text_ids.append(data[1])
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mask_ids.append(data[2])
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segment_ids.append(data[3])
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return label, text_ids, mask_ids, segment_ids
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def process_cmnli_clue_dataset(data_dir, label_list, bert_vocab_path,
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data_usage='train', shuffle_dataset=False, max_seq_len=128, batch_size=64):
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"""Process CMNLI dataset"""
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### Loading CMNLI from CLUEDataset
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assert data_usage in ['train', 'eval', 'test']
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if data_usage == 'train':
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dataset = ds.CLUEDataset(os.path.join(data_dir, "train.json"), task='CMNLI',
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usage=data_usage, shuffle=shuffle_dataset)
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elif data_usage == 'eval':
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dataset = ds.CLUEDataset(os.path.join(data_dir, "dev.json"), task='CMNLI',
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usage=data_usage, shuffle=shuffle_dataset)
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else:
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dataset = ds.CLUEDataset(os.path.join(data_dir, "test.json"), task='CMNLI',
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usage=data_usage, shuffle=shuffle_dataset)
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### Processing label
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if data_usage == 'test':
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dataset = dataset.map(input_columns=["id"], output_columns=["id", "label_id"],
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columns_order=["id", "label_id", "sentence1", "sentence2"], operations=ops.Duplicate())
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dataset = dataset.map(input_columns=["label_id"], operations=ops.Fill(0))
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else:
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label_vocab = text.Vocab.from_list(label_list)
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label_lookup = text.Lookup(label_vocab)
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dataset = dataset.map(input_columns="label", output_columns="label_id", operations=label_lookup)
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### Processing sentence pairs
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vocab = text.Vocab.from_file(bert_vocab_path)
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tokenizer = text.BertTokenizer(vocab, lower_case=True)
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lookup = text.Lookup(vocab, unknown_token='[UNK]')
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### Tokenizing sentences and truncate sequence pair
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dataset = dataset.map(input_columns=["sentence1"], operations=tokenizer)
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dataset = dataset.map(input_columns=["sentence2"], operations=tokenizer)
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dataset = dataset.map(input_columns=["sentence1", "sentence2"],
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operations=text.TruncateSequencePair(max_seq_len-3))
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### Adding special tokens
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dataset = dataset.map(input_columns=["sentence1"],
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operations=ops.Concatenate(prepend=np.array(["[CLS]"], dtype='S'),
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append=np.array(["[SEP]"], dtype='S')))
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dataset = dataset.map(input_columns=["sentence2"],
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operations=ops.Concatenate(append=np.array(["[SEP]"], dtype='S')))
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### Generating segment_ids
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dataset = dataset.map(input_columns=["sentence1"], output_columns=["sentence1", "type_sentence1"],
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columns_order=["sentence1", "type_sentence1", "sentence2", "label_id"],
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operations=ops.Duplicate())
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dataset = dataset.map(input_columns=["sentence2"], output_columns=["sentence2", "type_sentence2"],
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columns_order=["sentence1", "type_sentence1", "sentence2", "type_sentence2", "label_id"],
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operations=ops.Duplicate())
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dataset = dataset.map(input_columns=["type_sentence1"], operations=[lookup, ops.Fill(0)])
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dataset = dataset.map(input_columns=["type_sentence2"], operations=[lookup, ops.Fill(1)])
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dataset = dataset.map(input_columns=["type_sentence1", "type_sentence2"], output_columns=["segment_ids"],
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columns_order=["sentence1", "sentence2", "segment_ids", "label_id"],
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operations=ops.Concatenate())
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dataset = dataset.map(input_columns=["segment_ids"], operations=ops.PadEnd([max_seq_len], 0))
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### Generating text_ids
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dataset = dataset.map(input_columns=["sentence1", "sentence2"], output_columns=["text_ids"],
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columns_order=["text_ids", "segment_ids", "label_id"],
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operations=ops.Concatenate())
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dataset = dataset.map(input_columns=["text_ids"], operations=lookup)
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dataset = dataset.map(input_columns=["text_ids"], operations=ops.PadEnd([max_seq_len], 0))
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### Generating mask_ids
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dataset = dataset.map(input_columns=["text_ids"], output_columns=["text_ids", "mask_ids"],
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columns_order=["label_id", "text_ids", "mask_ids", "segment_ids"], operations=ops.Duplicate())
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dataset = dataset.map(input_columns=["mask_ids"], operations=ops.Mask(ops.Relational.NE, 0, mstype.int32))
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dataset = dataset.batch(batch_size)
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label = []
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text_ids = []
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mask_ids = []
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segment_ids = []
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for data in dataset:
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label.append(data[0])
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text_ids.append(data[1])
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mask_ids.append(data[2])
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segment_ids.append(data[3])
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return label, text_ids, mask_ids, segment_ids
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