| """ |
| 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() |
