| | """ |
| | 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 |
| |
|
| | from sentence_transformers import SentenceTransformer, SentencesDataset, losses, models, util |
| | from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator |
| | from sentence_transformers.readers import InputExample |
| |
|
| |
|
| | class PretrainedSTSbTest(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) |
| |
|
| | nli_dataset_path = 'datasets/AllNLI.tsv.gz' |
| | if not os.path.exists(nli_dataset_path): |
| | util.http_get('https://sbert.net/datasets/AllNLI.tsv.gz', nli_dataset_path) |
| |
|
| | |
| | label2int = {"contradiction": 0, "entailment": 1, "neutral": 2} |
| | self.nli_train_samples = [] |
| | max_train_samples = 10000 |
| | with gzip.open(nli_dataset_path, 'rt', encoding='utf8') as fIn: |
| | reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE) |
| | for row in reader: |
| | if row['split'] == 'train': |
| | label_id = label2int[row['label']] |
| | self.nli_train_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=label_id)) |
| | if len(self.nli_train_samples) >= max_train_samples: |
| | break |
| |
|
| | |
| | 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 = EmbeddingSimilarityEvaluator.from_input_examples(self.test_samples, name='sts-test') |
| | score = model.evaluate(evaluator)*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_train_stsb(self): |
| | word_embedding_model = models.Transformer('distilbert-base-uncased') |
| | pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension()) |
| | model = SentenceTransformer(modules=[word_embedding_model, pooling_model]) |
| | train_dataset = SentencesDataset(self.stsb_train_samples, model) |
| | train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=16) |
| | train_loss = losses.CosineSimilarityLoss(model=model) |
| | model.fit(train_objectives=[(train_dataloader, train_loss)], |
| | evaluator=None, |
| | epochs=1, |
| | evaluation_steps=1000, |
| | warmup_steps=int(len(train_dataloader)*0.1), |
| | use_amp=True) |
| |
|
| | self.evaluate_stsb_test(model, 80.0) |
| |
|
| | def test_train_nli(self): |
| | word_embedding_model = models.Transformer('distilbert-base-uncased') |
| | pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension()) |
| | model = SentenceTransformer(modules=[word_embedding_model, pooling_model]) |
| | train_dataset = SentencesDataset(self.nli_train_samples, model=model) |
| | train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=16) |
| | train_loss = losses.SoftmaxLoss(model=model, sentence_embedding_dimension=model.get_sentence_embedding_dimension(), num_labels=3) |
| | model.fit(train_objectives=[(train_dataloader, train_loss)], |
| | evaluator=None, |
| | epochs=1, |
| | warmup_steps=int(len(train_dataloader) * 0.1), |
| | use_amp=True) |
| |
|
| | self.evaluate_stsb_test(model, 50.0) |
| |
|
| |
|
| |
|
| | if "__main__" == __name__: |
| | unittest.main() |
