Hugging Face's logo
Seth L's picture
28 32

Seth L

splevine
21world's profile picture davidberenstein1957's profile picture victor's profile picture
·
https://mindfulmachines.substack.com/

AI & ML interests

NLP, Unsupervised Learning, Sentiment, Dialogue, Conversational AI, Topic Modeling, Visualization

Recent Activity

upvoted an article 6 days ago
Introducing Trackio: A Lightweight Experiment Tracking Library from Hugging Face
reacted to tomaarsen's post with 🤗 9 days ago
😎 I just published Sentence Transformers v5.1.0, and it's a big one. 2x-3x speedups of SparseEncoder models via ONNX and/or OpenVINO backends, easier distillation data preparation with hard negatives mining, and more: 1️⃣ Faster ONNX and OpenVINO backends for SparseEncoder models Usage is as simple as `backend="onnx"` or `backend="openvino"` when initializing a SparseEncoder to get started, but I also included utility functions for optimization, dynamic quantization, and static quantization, plus benchmarks. 2️⃣ New `n-tuple-scores` output format from `mine_hard_negatives` This new output format is immediately compatible with the MarginMSELoss and SparseMarginMSELoss for training SentenceTransformer, CrossEncoder, and SparseEncoder losses. 3️⃣ Gathering across devices When doing multi-GPU training using a loss that has in-batch negatives (e.g. MultipleNegativesRankingLoss), you can now use `gather_across_devices=True` to load in-batch negatives from the other devices too! Essentially a free lunch, pretty big impact potential in my evals. 4️⃣ Trackio support If you also upgrade `transformers`, and you install `trackio` with `pip install trackio`, then your experiments will also automatically be tracked locally with trackio. Just open up localhost and have a look at your losses/evals, no logins, no metric uploading. 5️⃣ MTEB Documentation We've added some documentation on evaluating SentenceTransformer models properly with MTEB. It's rudimentary as the documentation on the MTEB side is already great, but it should get you started. Plus many more smaller features & fixes (crash fixes, compatibility with datasets v4, FIPS compatibility, etc.). See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v5.1.0 Big thanks to all of the contributors for helping with the release, many of the features from this release were proposed by others. I have a big list of future potential features that I'd love to add, but I'm
reacted to tomaarsen's post with 🚀 9 days ago
😎 I just published Sentence Transformers v5.1.0, and it's a big one. 2x-3x speedups of SparseEncoder models via ONNX and/or OpenVINO backends, easier distillation data preparation with hard negatives mining, and more: 1️⃣ Faster ONNX and OpenVINO backends for SparseEncoder models Usage is as simple as `backend="onnx"` or `backend="openvino"` when initializing a SparseEncoder to get started, but I also included utility functions for optimization, dynamic quantization, and static quantization, plus benchmarks. 2️⃣ New `n-tuple-scores` output format from `mine_hard_negatives` This new output format is immediately compatible with the MarginMSELoss and SparseMarginMSELoss for training SentenceTransformer, CrossEncoder, and SparseEncoder losses. 3️⃣ Gathering across devices When doing multi-GPU training using a loss that has in-batch negatives (e.g. MultipleNegativesRankingLoss), you can now use `gather_across_devices=True` to load in-batch negatives from the other devices too! Essentially a free lunch, pretty big impact potential in my evals. 4️⃣ Trackio support If you also upgrade `transformers`, and you install `trackio` with `pip install trackio`, then your experiments will also automatically be tracked locally with trackio. Just open up localhost and have a look at your losses/evals, no logins, no metric uploading. 5️⃣ MTEB Documentation We've added some documentation on evaluating SentenceTransformer models properly with MTEB. It's rudimentary as the documentation on the MTEB side is already great, but it should get you started. Plus many more smaller features & fixes (crash fixes, compatibility with datasets v4, FIPS compatibility, etc.). See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v5.1.0 Big thanks to all of the contributors for helping with the release, many of the features from this release were proposed by others. I have a big list of future potential features that I'd love to add, but I'm
View all activity

Organizations

Agents-MCP-Hackathon's profile picture Loris's profile picture