!3009 Add a sample script of data processing for fine-tuning BERT on CLUE classification dataset

Merge pull request !3009 from dessyang/master

model_zoo/bert/src/clue_classification_dataset_process.py

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