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