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* Add machine reading comprehension model, preprocessor and pipeline * fix precommit errors * Optimize mrc preprocessor, add mrc input output definition, add mrc pipeline docstr --------- Co-authored-by: seadamo <ran.zhou@alibaba-inc.com>
60 lines
2.6 KiB
Python
60 lines
2.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.models import Model
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from modelscope.models.nlp import ModelForMachineReadingComprehension
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import MachineReadingComprehensionForNERPipeline
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from modelscope.preprocessors import \
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MachineReadingComprehensionForNERPreprocessor
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from modelscope.utils.constant import Tasks
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from modelscope.utils.test_utils import test_level
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class MachineReadingComprehensionTest(unittest.TestCase):
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sentence = 'Soccer - Japan get lucky win , China in surprise defeat .'
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model_id = 'damo/nlp_roberta_machine-reading-comprehension_for-ner'
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_mrc_for_ner_by_direct_model_download(self):
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cache_path = snapshot_download(self.model_id)
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tokenizer = MachineReadingComprehensionForNERPreprocessor(cache_path)
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model = ModelForMachineReadingComprehension.from_pretrained(cache_path)
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pipeline1 = MachineReadingComprehensionForNERPipeline(
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model, preprocessor=tokenizer)
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pipeline2 = pipeline(
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Tasks.machine_reading_comprehension,
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model=model,
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preprocessor=tokenizer)
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print(f'sentence: {self.sentence}\n'
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f'pipeline1:{pipeline1(input=self.sentence)}')
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print()
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print(f'pipeline2: {pipeline2(input=self.sentence)}')
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# {'ORG': [], 'PER': [], 'LOC': [' Japan', ' China'], 'MISC': []}
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_mrc_for_ner_with_model_from_modelhub(self):
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model = Model.from_pretrained(self.model_id)
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tokenizer = MachineReadingComprehensionForNERPreprocessor(
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model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.machine_reading_comprehension,
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model=model,
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preprocessor=tokenizer)
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print(f'sentence: {self.sentence}\n'
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f'pipeline:{pipeline_ins(input=self.sentence)}')
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# {'ORG': [], 'PER': [], 'LOC': [' Japan', ' China'], 'MISC': []}
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_mrc_for_ner_with_model_name(self):
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pipeline_ins = pipeline(
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task=Tasks.machine_reading_comprehension, model=self.model_id)
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print(pipeline_ins(input=self.sentence))
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# {'ORG': [], 'PER': [], 'LOC': [' Japan', ' China'], 'MISC': []}
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if __name__ == '__main__':
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unittest.main()
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