chandar-lab
/

NeoBERT

@@ -1,199 +1,96 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+license: mit
+datasets:
+- tiiuae/falcon-refinedweb
+language:
+- en
 ---
+# NeoBERT
+[![Hugging Face Model Card](https://img.shields.io/badge/Hugging%20Face-Model%20Card-blue)](https://huggingface.co/chandar-lab/NeoBERT)
+NeoBERT is a **next-generation encoder** model for English text representation, pre-trained from scratch on the RefinedWeb dataset. NeoBERT integrates state-of-the-art advancements in architecture, modern data, and optimized pre-training methodologies. It is designed for seamless adoption: it serves as a plug-and-play replacement for existing base models, relies on an **optimal depth-to-width ratio**, and leverages an extended context length of **4,096 tokens**. Despite its compact 250M parameter footprint, it is the most efficient model of its kind and achieves **state-of-the-art results** on the massive MTEB benchmark, outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under identical fine-tuning conditions.
+- Paper: [paper](https://arxiv.org/abs/2502.19587)
+- Repository: [github](https://github.com/chandar-lab/NeoBERT).
+## Get started
+Ensure you have the following dependencies installed:
+```bash
+pip install transformers torch xformers==0.0.28.post3
+```
+If you would like to use sequence packing (un-padding), you will need to also install flash-attention:
+```bash
+pip install transformers torch xformers==0.0.28.post3 flash_attn
+```
+## How to use
+Load the model using Hugging Face Transformers:
+```python
+from transformers import AutoModel, AutoTokenizer
+model_name = "chandar-lab/NeoBERT"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+# Tokenize input text
+text = "NeoBERT is the most efficient model of its kind!"
+inputs = tokenizer(text, return_tensors="pt")
+# Generate embeddings
+outputs = model(**inputs)
+embedding = outputs.last_hidden_state[:, 0, :]
+print(embedding.shape)
+```
+## Features
+| **Feature**       | **NeoBERT**                             |
+|---------------------------|-----------------------------|
+| `Depth-to-width`        | 28 × 768  |
+| `Parameter count`           | 250M                        |
+| `Activation`               | SwiGLU                      |
+| `Positional embeddings`     | RoPE                        |
+| `Normalization`            | Pre-RMSNorm                 |
+| `Data Source`              | RefinedWeb                  |
+| `Data Size`                | 2.8 TB                       |
+| `Tokenizer`                | google/bert                 |
+| `Context length`    | 4,096                       |
+| `MLM Masking Rate`             | 20%                         |
+| `Optimizer`                | AdamW                       |
+| `Scheduler`                | CosineDecay                 |
+| `Training Tokens`          | 2.1 T                        |
+| `Efficiency`               | FlashAttention              |
+## License
+Model weights and code repository are licensed under the permissive MIT license.
+## Citation
+If you use this model in your research, please cite:
+```bibtex
+@misc{breton2025neobertnextgenerationbert,
+      title={NeoBERT: A Next-Generation BERT},
+      author={Lola Le Breton and Quentin Fournier and Mariam El Mezouar and Sarath Chandar},
+      year={2025},
+      eprint={2502.19587},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.19587},
+}
+```
+## Contact
+For questions, do not hesitate to reach out and open an issue on here or on our **[GitHub](https://github.com/chandar-lab/NeoBERT)**.
+---