mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-18 17:27:43 +01:00
e6c05a2931
sentence-embedding模型支持finetune Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11537009
138 lines
7.5 KiB
Python
138 lines
7.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
||
import shutil
|
||
import unittest
|
||
|
||
from modelscope.hub.snapshot_download import snapshot_download
|
||
from modelscope.models import Model
|
||
from modelscope.models.nlp import BertForTextRanking
|
||
from modelscope.pipelines import pipeline
|
||
from modelscope.pipelines.nlp import TextRankingPipeline
|
||
from modelscope.preprocessors import TextRankingTransformersPreprocessor
|
||
from modelscope.utils.constant import Tasks
|
||
from modelscope.utils.test_utils import test_level
|
||
|
||
|
||
class TextRankingTest(unittest.TestCase):
|
||
base_model_id = 'damo/nlp_corom_passage-ranking_english-base'
|
||
tiny_model_id = 'damo/nlp_corom_passage-ranking_english-tiny'
|
||
inputs = {
|
||
'source_sentence': ["how long it take to get a master's degree"],
|
||
'sentences_to_compare': [
|
||
"On average, students take about 18 to 24 months to complete a master's degree.",
|
||
'On the other hand, some students prefer to go at a slower pace and choose to take '
|
||
'several years to complete their studies.',
|
||
'It can take anywhere from two semesters'
|
||
]
|
||
}
|
||
|
||
chinese_base_model_id = 'damo/nlp_rom_passage-ranking_chinese-base'
|
||
chinese_tiny_model_id = 'damo/nlp_corom_passage-ranking_chinese-tiny'
|
||
chinese_inputs = {
|
||
'source_sentence': ['功和功率的区别'],
|
||
'sentences_to_compare': [
|
||
'功反映做功多少，功率反映做功快慢。',
|
||
'什么是有功功率和无功功率?无功功率有什么用什么是有功功率和无功功率?无功功率有什么用电力系统中的电源是由发电机产生的三相正弦交流电,在交>流电路中,由电源供给负载的电功率有两种;一种是有功功率,一种是无功功率。',
|
||
'优质解答在物理学中,用电功率表示消耗电能的快慢．电功率用P表示,它的单位是瓦特（Watt）,简称瓦（Wa）符号是W.电流在单位时间内做的功叫做电功率 以灯泡为例,电功率越大,灯泡越亮.灯泡的亮暗由电功率（实际功率）\
|
||
决定,不由通过的电流、电压、电能决定!',
|
||
]
|
||
}
|
||
|
||
ecom_base_model_id = 'damo/nlp_corom_passage-ranking_chinese-base-ecom'
|
||
ecom_tiny_model_id = 'damo/nlp_corom_passage-ranking_chinese-tiny-ecom'
|
||
ecom_inputs = {
|
||
'source_sentence': ['毛绒玩具'],
|
||
'sentences_to_compare': ['大熊泰迪熊猫毛绒玩具公仔布娃娃抱抱熊', '背心式狗狗牵引绳']
|
||
}
|
||
|
||
medical_base_model_id = 'damo/nlp_corom_passage-ranking_chinese-base-medical'
|
||
medical_tiny_model_id = 'damo/nlp_corom_passage-ranking_chinese-tiny-medical'
|
||
medical_inputs = {
|
||
'source_sentence': ['肠道不适可以服用益生菌吗'],
|
||
'sentences_to_compare': ['肠胃不好能吃益生菌,益生菌有调节肠胃道菌群的作用', '身体发烧应该多喝水']
|
||
}
|
||
|
||
el_model_id = 'damo/nlp_bert_entity-matching_chinese-base'
|
||
el_inputs = {
|
||
'source_sentence': ['我是猫》([日]夏目漱石)【摘要 [ENT_S] 书评 [ENT_E] 试读】'],
|
||
'sentences_to_compare': [
|
||
'书评； 类型： Other； 别名： Book review; 三元组: 书评 # 外文名 # Book review $ 书评 # 摘要 # '
|
||
'书评，即评论并介绍书籍的文章，是以“书”为对象，实事求是的、有见识的分析书籍的形式和内容，探求创作的思想性、学术性、知识性和艺术性，从而在作者、读者和出版商之间构建信息交流的渠道。 $ 书评 # 定义 # '
|
||
'评论并介绍书籍的文章 $ 书评 # 中文名 # 书评 $ 书评 # 义项描述 # 书评 $ 书评 # 类型 # 应用写作的一种重要文体 $ 书评 # 标签 # 文学作品、文化、出版物、小说、书籍 $',
|
||
'摘要； 类型： Other； 别名： 摘， abstract， 书评; 三元组: 摘要 # 读音 # zhāi yào $ 摘要 # 外文名 # abstract $ 摘要 # 摘要 # '
|
||
'摘要又称概要、内容提要，意思是摘录要点或摘录下来的要点。 $ 摘要 # 词目 # 摘要 $ 摘要 # 词性 # 动词，名词 $ 摘要 # 中文名 # 摘要 $ 摘要 # 别称 # 概要、内容提要 $ 摘要 '
|
||
'# 义项描述 # 摘要 $ 摘要 # 标签 # 文化、文学家、行业人物、法律术语、小说 $',
|
||
]
|
||
}
|
||
|
||
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
||
def test_run_by_direct_model_download(self):
|
||
cache_path = snapshot_download(self.base_model_id)
|
||
tokenizer = TextRankingTransformersPreprocessor(cache_path)
|
||
model = BertForTextRanking.from_pretrained(cache_path)
|
||
pipeline1 = TextRankingPipeline(model, preprocessor=tokenizer)
|
||
pipeline2 = pipeline(
|
||
Tasks.text_ranking, model=model, preprocessor=tokenizer)
|
||
print(f'sentence: {self.inputs}\n'
|
||
f'pipeline1:{pipeline1(input=self.inputs)}')
|
||
print()
|
||
print(f'pipeline2:{pipeline2(input=self.inputs)}')
|
||
|
||
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
||
def test_run_with_model_from_modelhub(self):
|
||
model = Model.from_pretrained(self.base_model_id)
|
||
tokenizer = TextRankingTransformersPreprocessor(model.model_dir)
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=model, preprocessor=tokenizer)
|
||
print(pipeline_ins(input=self.inputs))
|
||
|
||
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
|
||
def test_run_with_model_name(self):
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.base_model_id)
|
||
print(pipeline_ins(input=self.inputs))
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.tiny_model_id)
|
||
print(pipeline_ins(input=self.inputs))
|
||
|
||
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
||
def test_run_with_default_model(self):
|
||
pipeline_ins = pipeline(task=Tasks.text_ranking)
|
||
print(pipeline_ins(input=self.inputs))
|
||
|
||
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
||
def test_run_chinese_model_with_model_name(self):
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.chinese_base_model_id)
|
||
print(pipeline_ins(input=self.chinese_inputs))
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.chinese_tiny_model_id)
|
||
print(pipeline_ins(input=self.chinese_inputs))
|
||
|
||
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
||
def test_run_ecom_model_with_model_name(self):
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.ecom_base_model_id)
|
||
print(pipeline_ins(input=self.ecom_inputs))
|
||
pipeline_tiny_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.ecom_tiny_model_id)
|
||
print(pipeline_tiny_ins(input=self.ecom_inputs))
|
||
|
||
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
||
def test_run_medical_model_with_model_name(self):
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.medical_base_model_id)
|
||
print(pipeline_ins(input=self.medical_inputs))
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.medical_tiny_model_id)
|
||
print(pipeline_ins(input=self.medical_inputs))
|
||
|
||
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
||
def test_run_with_el_model(self):
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.text_ranking, model=self.el_model_id)
|
||
print(pipeline_ins(input=self.el_inputs))
|
||
|
||
|
||
if __name__ == '__main__':
|
||
unittest.main()
|