lightonai/modernbert-embed-large-unsupervised

🤗 Hugging Face 来源sentence-similarityapache-2.0395M 参数1.6 GBsafetensors✓ 1 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo lightonai/modernbert-embed-large-unsupervised ./model-folder
需要做种者 →

ModernBERT-embed-large-unsupervised

modernbert-embed-unsupervised-large is the unsupervised checkpoint trained with the contrastors library for 1 epoch over the 235M weakly-supervised contrastive pairs curated in Nomic Embed.

We suggest using moderbert-embed-large for embedding tasks.

Performance

Model Average (56) Classification (12) Clustering (11) Pair Classification (3) Reranking (4) Retrieval (15) STS (10) Overall
nomic-embed-text-v1_unsup 59.9 71.2 42.5 83.7 55.0 48.0 80.8 30.7
modernbert-embed-base-unsupervised 60.03 72.11 44.34 82.78 55.0 47.05 80.33 31.2
modernbert-embed-large-unsupervised 60.71 72.90 44.96 83.44 55.54 47.90 80.95 29.86

Acknowledgment

We wanted to thank Zach Nussbaum from Nomic AI for building and sharing the Nomic Embed recipe and tools and its support during the training of this model!

The training has been run on Orange Business Cloud Avenue infrastructure.

Citation

If you find the model, dataset, or training code useful, please considering citing ModernBERT as well as Nomic Embed:

@misc{modernbert,
      title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference}, 
      author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli},
      year={2024},
      eprint={2412.13663},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.13663}, 
}
@misc{nussbaum2024nomic,
      title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, 
      author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
      year={2024},
      eprint={2402.01613},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

And if you want to cite this fine-tuning in particular, please use:

@misc{ModernBERT-embed-large,
  title={ModernBERT-embed-large},
  author={Chaffin, Antoine},
  url={https://huggingface.co/lightonai/modernbert-embed-large},
  year={2025}
}