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Tom Aarsen

tomaarsen

https://linkedin.com/in/tomaarsen

AI & ML interests

NLP: text embeddings, information retrieval, named entity recognition, few-shot text classification

Recent Activity

new activity about 17 hours ago

answerdotai/ModernBERT-base:Unpadding and Sequence Packing inference example?

posted an update about 17 hours ago

That didn't take long! Nomic AI has finetuned the new ModernBERT-base encoder model into a strong embedding model for search, classification, clustering and more! Details: 🤖 Based on ModernBERT-base with 149M parameters. 📊 Outperforms both nomic-embed-text-v1 and nomic-embed-text-v1.5 on MTEB! 🏎️ Immediate FA2 and unpacking support for super efficient inference. 🪆 Trained with Matryoshka support, i.e. 2 valid output dimensionalities: 768 and 256. ➡️ Maximum sequence length of 8192 tokens! 2️⃣ Trained in 2 stages: unsupervised contrastive data -> high quality labeled datasets. ➕ Integrated in Sentence Transformers, Transformers, LangChain, LlamaIndex, Haystack, etc. 🏛️ Apache 2.0 licensed: fully commercially permissible Try it out here: https://huggingface.co/nomic-ai/modernbert-embed-base Very nice work by Zach Nussbaum and colleagues at Nomic AI.

liked a model 1 day ago

nomic-ai/modernbert-embed-base

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Articles

Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

Mar 22, 2024

• 69

🪆 Introduction to Matryoshka Embedding Models

Feb 23, 2024

• 63

SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit

Dec 6, 2023

• 6

🕳️ Attention Sinks in LLMs for endless fluency

Oct 9, 2023

• 7

Organizations

Posts 17

Post

486

That didn't take long! Nomic AI has finetuned the new ModernBERT-base encoder model into a strong embedding model for search, classification, clustering and more!

Details:
🤖 Based on ModernBERT-base with 149M parameters.
📊 Outperforms both nomic-embed-text-v1 and nomic-embed-text-v1.5 on MTEB!
🏎️ Immediate FA2 and unpacking support for super efficient inference.
🪆 Trained with Matryoshka support, i.e. 2 valid output dimensionalities: 768 and 256.
➡️ Maximum sequence length of 8192 tokens!
2️⃣ Trained in 2 stages: unsupervised contrastive data -> high quality labeled datasets.
➕ Integrated in Sentence Transformers, Transformers, LangChain, LlamaIndex, Haystack, etc.
🏛️ Apache 2.0 licensed: fully commercially permissible

Try it out here: nomic-ai/modernbert-embed-base

Very nice work by Zach Nussbaum and colleagues at Nomic AI.

Post

5385

I just released Sentence Transformers v3.3.0 & it's huge! 4.5x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free perf. boost, PEFT integration, evaluation on NanoBEIR, and more! Details:

1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new export_static_quantized_openvino_model method to quantize a model.

2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the prompts argument in SentenceTransformerTrainingArguments. Our experiments show that you can easily reach 0.66% to 0.90% relative performance improvement on NDCG@10 at no extra cost by adding "query: " before each training query and "document: " before each training answer.

3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model.

4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks.

Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0

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