This is a smaller release with some new features

MarginMSELoss

MarginMSELoss is a great method to train embeddings model with the help of a cross-encoder model. The details are explained here: MSMARCO - MarginMSE Training

You pass your training data in the format:

InputExample(texts=[query, positive, negative], label=cross_encoder.predict([query, positive])-cross_encoder.predict([query, negative])

MultipleNegativesSymmetricRankingLoss

MultipleNegativesRankingLoss computes the loss just in one way: Find the correct answer for a given question.

MultipleNegativesSymmetricRankingLoss also computes the loss in the other direction: Find the correct question for a given answer.

Breaking Change: CLIPModel

The CLIPModel is now based on the transformers model.

You can still load it like this:

model = SentenceTransformer('clip-ViT-B-32')

Older SentenceTransformers versions are now longer able to load and use the 'clip-ViT-B-32' model.

Added files on the hub are automatically downloaded

PR #1116 checks if you have all files in your local cache or if there are added files on the hub. If this is the case, it will automatically download them.

SentenceTransformers.encode() can return all values

When you set output_value=None for the encode method, all values (token_ids, token_embeddings, sentence_embedding) will be returned.