huggingface--transformers
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78 行
2.6 KiB
Python
78 行
2.6 KiB
Python
# Copyright 2022 The OpenBMB Team and The HuggingFace Inc. team.
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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 os
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import shutil
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import tempfile
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import unittest
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from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
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from transformers.testing_utils import require_rjieba, tooslow
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from ...test_tokenization_common import TokenizerTesterMixin
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@require_rjieba
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class CPMAntTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "hf-internal-testing/cpm-ant-10b-testing"
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tokenizer_class = CpmAntTokenizer
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test_rust_tokenizer = False
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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old_tmpdirname = cls.tmpdirname
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cls.tmpdirname = tempfile.mkdtemp()
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vocab_tokens = [
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"<d>",
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"</d>",
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"<s>",
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"</s>",
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"</_>",
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"<unk>",
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"<pad>",
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"</n>",
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"我",
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"是",
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"C",
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"P",
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"M",
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"A",
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"n",
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"t",
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]
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cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(cls.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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shutil.rmtree(old_tmpdirname, ignore_errors=True)
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@tooslow
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def test_pre_tokenization(self):
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tokenizer = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b")
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texts = "今天天气真好！"
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rjieba_tokens = ["今天", "天气", "真", "好", "！"]
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tokens = tokenizer.tokenize(texts)
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self.assertListEqual(tokens, rjieba_tokens)
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normalized_text = "今天天气真好！"
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input_tokens = [tokenizer.bos_token] + tokens
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input_rjieba_tokens = [6, 9802, 14962, 2082, 831, 244]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_rjieba_tokens)
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reconstructed_text = tokenizer.decode(input_rjieba_tokens)
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self.assertEqual(reconstructed_text, normalized_text)
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