- Add support training and using CrossEncoder
- Data Augmentation method AugSBERT added
- New model trained on large scale paraphrase data. Models works on internal benchmark much better than previous models: distilroberta-base-paraphrase-v1 and xlm-r-distilroberta-base-paraphrase-v1
- New model for Information Retrieval trained on MS Marco: distilroberta-base-msmarco-v1
- Improved MultipleNegativesRankingLoss loss function: Similarity function can be changed and is now cosine similarity (was dot-product before), further, similarity scores can be multiplied by a scaling factor. This allows the usage of NTXentLoss / InfoNCE loss.
- New MegaBatchMarginLoss, inspired from the paper ParaNMT-Paper.
Smaller changes:
- Update InformationRetrievalEvaluator, so that it can work with large corpora (Millions of entries). Removed the query_chunk_size parameter from the evaluator
- SentenceTransformer.encode method detaches tensors from compute graph
- SentenceTransformer.fit() method - Parameter output_path_ignore_not_empty deprecated. No longer checks that target folder must be empty