def METHOD_NAME(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)