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JT

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liked a Space about 2 hours ago

google/embeddinggemma-tuning-lab

liked a model 6 days ago

kyutai/pocket-tts

reacted to tomaarsen's post with 🔥 about 1 month ago

🐦‍🔥 I've just published Sentence Transformers v5.2.0! It introduces multi-processing for CrossEncoder (rerankers), multilingual NanoBEIR evaluators, similarity score outputs in mine_hard_negatives, Transformers v5 support and more. Details: - CrossEncoder multi-processing: Similar to SentenceTransformer and SparseEncoder, you can now use multi-processing with CrossEncoder rerankers. Useful for multi-GPU and CPU settings, and simple to configure: just `device=["cuda:0", "cuda:1"]` or `device=["cpu"]*4` on the `model.predict` or `model.rank` calls. - Multilingual NanoBEIR Support: You can now use community translations of the tiny NanoBEIR retrieval benchmark instead of only the English one, by passing `dataset_id`, e.g. `dataset_id="lightonai/NanoBEIR-de"` for the German benchmark. - Similarity scores in Hard Negatives Mining: When mining for hard negatives to create a strong training dataset, you can now pass `output_scores=True` to get similarity scores returned. This can be useful for some distillation losses! - Transformers v5: This release works with both Transformers v4 and the upcoming v5. In the future, Sentence Transformers will only work with Transformers v5, but not yet! - Python 3.9 deprecation: Now that Python 3.9 has lost security support, Sentence Transformers no longer supports it. Check out the full changelog for more details: https://github.com/huggingface/sentence-transformers/releases/tag/v5.2.0 I'm quite excited about what's coming. There's a huge draft PR with a notable refactor in the works that should bring some exciting support. Specifically, better multimodality, rerankers, and perhaps some late interaction in the future!

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