| """ |
| Tests that the pretrained models produce the correct scores on the STSbenchmark dataset |
| """ |
| import csv |
| import gzip |
| import os |
| import unittest |
|
|
| from torch.utils.data import DataLoader |
| import logging |
| from sentence_transformers import CrossEncoder, util, LoggingHandler |
| from sentence_transformers.readers import InputExample |
| from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator |
|
|
|
|
|
|
| class CrossEncoderTest(unittest.TestCase): |
| def setUp(self): |
| sts_dataset_path = 'datasets/stsbenchmark.tsv.gz' |
| if not os.path.exists(sts_dataset_path): |
| util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path) |
|
|
| |
| self.stsb_train_samples = [] |
| self.dev_samples = [] |
| self.test_samples = [] |
| with gzip.open(sts_dataset_path, 'rt', encoding='utf8') as fIn: |
| reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE) |
| for row in reader: |
| score = float(row['score']) / 5.0 |
| inp_example = InputExample(texts=[row['sentence1'], row['sentence2']], label=score) |
|
|
| if row['split'] == 'dev': |
| self.dev_samples.append(inp_example) |
| elif row['split'] == 'test': |
| self.test_samples.append(inp_example) |
| else: |
| self.stsb_train_samples.append(inp_example) |
|
|
| def evaluate_stsb_test(self, model, expected_score): |
| evaluator = CECorrelationEvaluator.from_input_examples(self.test_samples, name='sts-test') |
| score = evaluator(model)*100 |
| print("STS-Test Performance: {:.2f} vs. exp: {:.2f}".format(score, expected_score)) |
| assert score > expected_score or abs(score-expected_score) < 0.1 |
|
|
| def test_pretrained_stsb(self): |
| model = CrossEncoder("cross-encoder/stsb-distilroberta-base") |
| self.evaluate_stsb_test(model, 87.92) |
|
|
| def test_train_stsb(self): |
| model = CrossEncoder('distilroberta-base', num_labels=1) |
| train_dataloader = DataLoader(self.stsb_train_samples, shuffle=True, batch_size=16) |
| model.fit(train_dataloader=train_dataloader, |
| epochs=1, |
| warmup_steps=int(len(train_dataloader)*0.1)) |
| self.evaluate_stsb_test(model, 75) |
|
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|
|
|
| if "__main__" == __name__: |
| unittest.main() |
