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29fef1e7aa
* nits * Fixing deps. * Ruff update. * Import order matters. * Fix. * Revert ruff fix. * Visualizer. * Putting back the imports. --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
from tokenizers import BertWordPieceTokenizer
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from ..utils import bert_files, data_dir, multiprocessing_with_parallelism
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class TestBertWordPieceTokenizer:
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def test_basic_encode(self, bert_files):
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tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"])
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# Encode with special tokens by default
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output = tokenizer.encode("My name is John", "pair")
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assert output.ids == [101, 2026, 2171, 2003, 2198, 102, 3940, 102]
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assert output.tokens == [
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"[CLS]",
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"my",
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"name",
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"is",
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"john",
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"[SEP]",
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"pair",
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"[SEP]",
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]
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assert output.offsets == [
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(0, 0),
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(0, 2),
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(3, 7),
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(8, 10),
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(11, 15),
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(0, 0),
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(0, 4),
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(0, 0),
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]
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assert output.type_ids == [0, 0, 0, 0, 0, 0, 1, 1]
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# Can encode without the special tokens
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output = tokenizer.encode("My name is John", "pair", add_special_tokens=False)
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assert output.ids == [2026, 2171, 2003, 2198, 3940]
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assert output.tokens == ["my", "name", "is", "john", "pair"]
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assert output.offsets == [(0, 2), (3, 7), (8, 10), (11, 15), (0, 4)]
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assert output.type_ids == [0, 0, 0, 0, 1]
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def test_multiprocessing_with_parallelism(self, bert_files):
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tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"])
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multiprocessing_with_parallelism(tokenizer, False)
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multiprocessing_with_parallelism(tokenizer, True)
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def test_train_from_iterator(self):
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text = ["A first sentence", "Another sentence", "And a last one"]
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tokenizer = BertWordPieceTokenizer()
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tokenizer.train_from_iterator(text, show_progress=False)
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output = tokenizer.encode("A sentence")
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assert output.tokens == ["a", "sentence"]
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