wehub-resource-sync
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
# Copyright (c) 2020 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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def convert_example(example, tokenizer, is_test=False):
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"""
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Builds model inputs from a sequence for sequence classification tasks.
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It use `jieba.cut` to tokenize text.
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Args:
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example(obj:`list[str]`): List of input data, containing text and label if it have label.
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tokenizer(obj: paddlenlp.data.JiebaTokenizer): It use jieba to cut the chinese string.
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is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.
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Returns:
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query_ids(obj:`list[int]`): The list of query ids.
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title_ids(obj:`list[int]`): The list of title ids.
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query_seq_len(obj:`int`): The input sequence query length.
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title_seq_len(obj:`int`): The input sequence title length.
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label(obj:`numpy.array`, data type of int64, optional): The input label if not is_test.
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"""
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query, title = example["query"], example["title"]
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query_ids = np.array(tokenizer.encode(query), dtype="int64")
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query_seq_len = np.array(len(query_ids), dtype="int64")
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title_ids = np.array(tokenizer.encode(title), dtype="int64")
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title_seq_len = np.array(len(title_ids), dtype="int64")
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if not is_test:
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label = np.array(example["label"], dtype="int64")
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return query_ids, title_ids, query_seq_len, title_seq_len, label
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else:
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return query_ids, title_ids, query_seq_len, title_seq_len
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def preprocess_prediction_data(data, tokenizer):
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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[List[str, str]]`):
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The prediction data whose each element is a text pair.
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Each text will be tokenized by jieba.lcut() function.
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tokenizer(obj: paddlenlp.data.JiebaTokenizer): It use jieba to cut the chinese string.
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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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- query_ids(obj:`list[int]`): The list of query ids.
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- title_ids(obj:`list[int]`): The list of title ids.
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- query_seq_len(obj:`int`): The input sequence query length.
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- title_seq_len(obj:`int`): The input sequence title length.
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"""
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examples = []
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for query, title in data:
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query_ids = tokenizer.encode(query)
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title_ids = tokenizer.encode(title)
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examples.append([query_ids, title_ids, len(query_ids), len(title_ids)])
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return examples
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