The usage is simple:
```python
from sentence_transformers import evaluation
sentences1 = ['This list contains the first column', 'With your sentences', 'You want your model to evaluate on']
sentences2 = ['Sentences contains the other column', 'The evaluator matches sentences1[i] with sentences2[i]', 'Compute the cosine similarity and compares it to scores[i]']
scores = [0.3, 0.6, 0.2]
evaluator = evaluation.EmbeddingSimilarityEvaluator(sentences1, sentences2, scores)
... Your other code to load training data
model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=1, warmup_steps=100, evaluator=evaluator, evaluation_steps=500)
```
Continue Training on Other Data
training_stsbenchmark_continue_training.py shows an example where training on a fine-tuned model is continued. In that example, we use a sentence transformer model that was first fine-tuned on the NLI dataset and then continue training on the training data from the STS benchmark.
