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d721fabb34
1.新增支持原始bert模型（非easynlp的 backbone prefix版本）
2.支持bert的在sequence classification/fill mask /token classification上的backbone head形式
3.统一了sequence classification几个任务的pipeline到一个类
4.fill mask 支持backbone head形式
5.token classification的几个子任务（ner，word seg， part of speech）的preprocessor 统一到了一起TokenClassificationPreprocessor
6. sequence classification的几个子任务（single classification， pair classification）的preprocessor 统一到了一起SequenceClassificationPreprocessor
7. 改动register中 cls的group_key 赋值位置，之前的group_key在多个decorators的情况下，会被覆盖，obj_cls的group_key信息不正确
8. 基于backbone head形式将 原本group_key和 module同名的情况尝试做调整，如下在modelscope/pipelines/nlp/sequence_classification_pipeline.py 中
原本
@PIPELINES.register_module(
Tasks.sentiment_classification, module_name=Pipelines.sentiment_classification)
改成
@PIPELINES.register_module(
Tasks.text_classification, module_name=Pipelines.sentiment_classification)
相应的configuration.json也有改动，这样的改动更符合任务和pipline（子任务）的关系。
8. 其他相应改动为支持上述功能
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10041463
61 lines
2.5 KiB
Python
61 lines
2.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import torch
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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 DebertaV2ForMaskedLM
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import FillMaskPipeline
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from modelscope.preprocessors import NLPPreprocessor
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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 DeBERTaV2TaskTest(unittest.TestCase):
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model_id_deberta = 'damo/nlp_debertav2_fill-mask_chinese-lite'
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ori_text = '你师父差得动你，你师父可差不动我。'
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test_input = '你师父差得动你，你师父可[MASK]不动我。'
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_by_direct_model_download(self):
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model_dir = snapshot_download(self.model_id_deberta)
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preprocessor = NLPPreprocessor(
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model_dir, first_sequence='sentence', second_sequence=None)
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model = DebertaV2ForMaskedLM.from_pretrained(model_dir)
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pipeline1 = FillMaskPipeline(model, preprocessor)
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pipeline2 = pipeline(
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Tasks.fill_mask, model=model, preprocessor=preprocessor)
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ori_text = self.ori_text
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test_input = self.test_input
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print(f'\nori_text: {ori_text}\ninput: {test_input}\npipeline1: '
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f'{pipeline1(test_input)}\npipeline2: {pipeline2(test_input)}\n')
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_from_modelhub(self):
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# sbert
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print(self.model_id_deberta)
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model = Model.from_pretrained(self.model_id_deberta)
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preprocessor = NLPPreprocessor(
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model.model_dir, first_sequence='sentence', second_sequence=None)
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pipeline_ins = pipeline(
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task=Tasks.fill_mask, model=model, preprocessor=preprocessor)
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print(
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f'\nori_text: {self.ori_text}\ninput: {self.test_input}\npipeline: '
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f'{pipeline_ins(self.test_input)}\n')
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_name(self):
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pipeline_ins = pipeline(
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task=Tasks.fill_mask, model=self.model_id_deberta)
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ori_text = self.ori_text
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test_input = self.test_input
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print(f'\nori_text: {ori_text}\ninput: {test_input}\npipeline: '
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f'{pipeline_ins(test_input)}\n')
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if __name__ == '__main__':
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unittest.main()
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