mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-17 00:37:43 +01:00
102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import shutil
|
|
import unittest
|
|
|
|
from modelscope.models import Model
|
|
from modelscope.pipelines import SequenceClassificationPipeline, pipeline
|
|
from modelscope.preprocessors import SequenceClassificationPreprocessor
|
|
from modelscope.pydatasets import PyDataset
|
|
from modelscope.utils.constant import Hubs, Tasks
|
|
from modelscope.utils.test_utils import test_level
|
|
|
|
|
|
class SequenceClassificationTest(unittest.TestCase):
|
|
|
|
def setUp(self) -> None:
|
|
self.model_id = 'damo/bert-base-sst2'
|
|
|
|
def predict(self, pipeline_ins: SequenceClassificationPipeline):
|
|
from easynlp.appzoo import load_dataset
|
|
|
|
set = load_dataset('glue', 'sst2')
|
|
data = set['test']['sentence'][:3]
|
|
|
|
results = pipeline_ins(data[0])
|
|
print(results)
|
|
results = pipeline_ins(data[1])
|
|
print(results)
|
|
|
|
print(data)
|
|
|
|
def printDataset(self, dataset: PyDataset):
|
|
for i, r in enumerate(dataset):
|
|
if i > 10:
|
|
break
|
|
print(r)
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_run_with_model_from_modelhub(self):
|
|
model = Model.from_pretrained(self.model_id)
|
|
preprocessor = SequenceClassificationPreprocessor(
|
|
model.model_dir, first_sequence='sentence', second_sequence=None)
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.text_classification,
|
|
model=model,
|
|
preprocessor=preprocessor)
|
|
self.predict(pipeline_ins)
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_run_with_model_name(self):
|
|
text_classification = pipeline(
|
|
task=Tasks.text_classification, model=self.model_id)
|
|
result = text_classification(
|
|
PyDataset.load(
|
|
'glue',
|
|
subset_name='sst2',
|
|
split='train',
|
|
target='sentence',
|
|
hub=Hubs.huggingface))
|
|
self.printDataset(result)
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_default_model(self):
|
|
text_classification = pipeline(task=Tasks.text_classification)
|
|
result = text_classification(
|
|
PyDataset.load(
|
|
'glue',
|
|
subset_name='sst2',
|
|
split='train',
|
|
target='sentence',
|
|
hub=Hubs.huggingface))
|
|
self.printDataset(result)
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_dataset(self):
|
|
model = Model.from_pretrained(self.model_id)
|
|
preprocessor = SequenceClassificationPreprocessor(
|
|
model.model_dir, first_sequence='sentence', second_sequence=None)
|
|
text_classification = pipeline(
|
|
Tasks.text_classification, model=model, preprocessor=preprocessor)
|
|
# loaded from huggingface dataset
|
|
dataset = PyDataset.load(
|
|
'glue',
|
|
subset_name='sst2',
|
|
split='train',
|
|
target='sentence',
|
|
hub=Hubs.huggingface)
|
|
result = text_classification(dataset)
|
|
self.printDataset(result)
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_modelscope_dataset(self):
|
|
text_classification = pipeline(task=Tasks.text_classification)
|
|
# loaded from modelscope dataset
|
|
dataset = PyDataset.load(
|
|
'squad', split='train', target='context', hub=Hubs.modelscope)
|
|
result = text_classification(dataset)
|
|
self.printDataset(result)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|