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52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import json
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import torch
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from modelscope.models import Model
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from modelscope.models.nlp import DocumentGroundedDialogRerankModel
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from modelscope.msdatasets import MsDataset
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from modelscope.pipelines.nlp import DocumentGroundedDialogRerankPipeline
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from modelscope.preprocessors.nlp import \
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DocumentGroundedDialogRerankPreprocessor
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from modelscope.utils.constant import DownloadMode, Tasks
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from modelscope.utils.test_utils import test_level
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class DocumentGroundedDialogRerankTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.document_grounded_dialog_rerank
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self.model_id = 'DAMO_ConvAI/nlp_convai_ranking_pretrain'
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run(self):
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args = {
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'output': '../../../result.json',
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'max_batch_size': 64,
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'exclude_instances': '',
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'include_passages': False,
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'do_lower_case': True,
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'max_seq_length': 512,
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'query_length': 195,
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'tokenizer_resize': True,
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'model_resize': True,
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'kilt_data': True
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}
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model = Model.from_pretrained(self.model_id, revision='v1.0.0', **args)
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mypreprocessor = DocumentGroundedDialogRerankPreprocessor(
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model.model_dir, **args)
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pipeline_ins = DocumentGroundedDialogRerankPipeline(
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model=model, preprocessor=mypreprocessor, **args)
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dataset = MsDataset.load(
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'DAMO_ConvAI/FrDoc2BotRerank',
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download_mode=DownloadMode.FORCE_REDOWNLOAD,
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split='test')[:2]
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# print(dataset)
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pipeline_ins(dataset)
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if __name__ == '__main__':
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unittest.main()
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