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Youngjoon Jang

yjoonjang

yjoonjang

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liked a model about 2 months ago

annamodels/LGAI-Embedding-Preview

updated a dataset about 2 months ago

yjoonjang/squad_kor_v1

published a dataset about 2 months ago

yjoonjang/squad_kor_v1

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liked a model about 2 months ago

annamodels/LGAI-Embedding-Preview

updated a dataset about 2 months ago

yjoonjang/squad_kor_v1

Viewer • Updated Jun 7 • 12.5k • 7

published a dataset about 2 months ago

yjoonjang/squad_kor_v1

Viewer • Updated Jun 7 • 12.5k • 7

New activity in Qwen/Qwen3-Embedding-0.6B 2 months ago

Add sentence_bert_config.json

#7 opened 2 months ago by

yjoonjang

Add sentence_bert_config.json

#8 opened 2 months ago by

yjoonjang

liked 2 models 2 months ago

Qwen/Qwen3-Embedding-0.6B

Feature Extraction • 0.6B • Updated Jun 20 • 2.92M • • 446

Qwen/Qwen3-Embedding-0.6B-GGUF

0.6B • Updated 22 days ago • 29.8k • 427

liked a model 3 months ago

reasonir/ReasonIR-8B

Feature Extraction • 8B • Updated May 13 • 6.62k • 53

upvoted a paper 3 months ago

Crosslingual Reasoning through Test-Time Scaling

Paper • 2505.05408 • Published May 8 • 8

liked 2 models 3 months ago

lightonai/GTE-ModernColBERT-v1

yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview

8B • Updated Apr 28 • 1.37k • 33

upvoted a collection 3 months ago

HyperCLOVA X SEED

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HyperCLOVA X SEED is NAVER's lightweight open-source lineup with a strong focus on Korean language performance • 4 items • Updated 15 days ago • 28

liked a model 3 months ago

naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B

2B • Updated 16 days ago • 12.6k • 131

reacted to tomaarsen's post with 🔥 4 months ago

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4123

I just released Sentence Transformers v4.1; featuring ONNX and OpenVINO backends for rerankers offering 2-3x speedups and improved hard negatives mining which helps prepare stronger training datasets. Details:

🏎️ ONNX, OpenVINO, Optimization, Quantization
- I've added ONNX and OpenVINO support with just one extra argument: "backend" when loading the CrossEncoder reranker, e.g.: CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2", backend="onnx")
- The export_optimized_onnx_model, export_dynamic_quantized_onnx_model, and export_static_quantized_openvino_model functions now work with CrossEncoder rerankers, allowing you to optimize (e.g. fusions, gelu approximations, etc.) or quantize (int8 weights) rerankers.
- I've uploaded ~340 ONNX & OpenVINO models for all existing models under the cross-encoder Hugging Face organization. You can use these without having to export when loading.

⛏ Improved Hard Negatives Mining
- Added 'absolute_margin' and 'relative_margin' arguments to mine_hard_negatives.
- absolute_margin ensures that sim(query, negative) < sim(query, positive) - absolute_margin, i.e. an absolute margin between the negative & positive similarities.
- relative_margin ensures that sim(query, negative) < sim(query, positive) * (1 - relative_margin), i.e. a relative margin between the negative & positive similarities.
- Inspired by the excellent NV-Retriever paper from NVIDIA.

And several other small improvements. Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v4.1.0

With this release, I introduce near-feature parity between the SentenceTransformer embedding & CrossEncoder reranker models, which I've wanted to do for quite some time! With rerankers very strongly supported now, it's time to look forward to other useful architectures!