Sentence Transformers

👉 What happened in AI in 2025? 👈

We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!

Play with it here:
2025-ai-timeline/2025-ai-timeline

Here's my personal quarterly TL;DR:

1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.

Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)

2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.

Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4

3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.

Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5

4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!

Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯

Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline

🫡 @reach-vb for the original idea, design and recipe

🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition

🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂

2 replies

tomaarsen

updated a model 2 months ago

sentence-transformers/all-MiniLM-L12-v2

tomaarsen

in sentence-transformers/all-MiniLM-L12-v2 2 months ago

Update README.md

#20 opened 2 months ago by

Aboukhdoud

tomaarsen

updated a collection 2 months ago

NanoBEIR datasets

Collection

These datasets are compatible with the (Sparse)NanoBEIREvaluator with Sentence Transformers v5.2+. Also CrossEncoderNanoBEIREvaluator if bm25 column • 18 items • Updated 16 days ago • 14

tomaarsen

posted an update 2 months ago

Post

3681

🐦‍🔥 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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Quantized Retrieval

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