mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-18 17:27:43 +01:00
83 lines
3.7 KiB
Python
83 lines
3.7 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import unittest
|
|
|
|
from modelscope.hub.snapshot_download import snapshot_download
|
|
from modelscope.models import Model
|
|
from modelscope.pipelines import pipeline
|
|
from modelscope.pipelines.nlp import TextClassificationPipeline
|
|
from modelscope.preprocessors import TextClassificationTransformersPreprocessor
|
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.utils.regress_test_utils import IgnoreKeyFn, MsRegressTool
|
|
from modelscope.utils.test_utils import test_level
|
|
|
|
|
|
class NLITest(unittest.TestCase):
|
|
|
|
def setUp(self) -> None:
|
|
self.task = Tasks.nli
|
|
self.model_id = 'damo/nlp_structbert_nli_chinese-base'
|
|
self.model_id_fact_checking = 'damo/nlp_structbert_fact-checking_chinese-base'
|
|
self.model_id_peer = 'damo/nlp_peer_mnli_english-base'
|
|
|
|
sentence1 = '四川商务职业学院和四川财经职业学院哪个好？'
|
|
sentence2 = '四川商务职业学院商务管理在哪个校区？'
|
|
en_sentence1 = 'Conceptually cream skimming has two basic dimensions - product and geography.'
|
|
en_sentence2 = 'Product and geography are what make cream skimming work.'
|
|
regress_tool = MsRegressTool(baseline=False)
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_direct_file_download(self):
|
|
cache_path = snapshot_download(self.model_id)
|
|
tokenizer = TextClassificationTransformersPreprocessor(cache_path)
|
|
model = Model.from_pretrained(cache_path)
|
|
pipeline1 = TextClassificationPipeline(model, preprocessor=tokenizer)
|
|
pipeline2 = pipeline(Tasks.nli, model=model, preprocessor=tokenizer)
|
|
print(f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n'
|
|
f'pipeline1:{pipeline1(input=(self.sentence1, self.sentence2))}')
|
|
print(
|
|
f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n'
|
|
f'pipeline1: {pipeline2(input=(self.sentence1, self.sentence2))}')
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_model_from_modelhub(self):
|
|
model = Model.from_pretrained(self.model_id)
|
|
tokenizer = TextClassificationTransformersPreprocessor(model.model_dir)
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.nli, model=model, preprocessor=tokenizer)
|
|
print(pipeline_ins(input=(self.sentence1, self.sentence2)))
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_run_with_model_name(self):
|
|
pipeline_ins = pipeline(task=Tasks.nli, model=self.model_id)
|
|
with self.regress_tool.monitor_module_single_forward(
|
|
pipeline_ins.model,
|
|
'sbert_nli',
|
|
compare_fn=IgnoreKeyFn('.*intermediate_act_fn')):
|
|
print(pipeline_ins(input=(self.sentence1, self.sentence2)))
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_fact_checking_model(self):
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.nli,
|
|
model=self.model_id_fact_checking,
|
|
model_revision='v1.0.1')
|
|
print(pipeline_ins(input=(self.sentence1, self.sentence2)))
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_peer_model(self):
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.nli,
|
|
model=self.model_id_peer,
|
|
model_revision='v1.0.0',
|
|
)
|
|
print(pipeline_ins(input=(self.en_sentence1, self.en_sentence2)))
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_default_model(self):
|
|
pipeline_ins = pipeline(task=Tasks.nli)
|
|
print(pipeline_ins(input=(self.sentence1, self.sentence2)))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|