Model reference · open weights

modernbert-embed-large-unsupervised

Available as managed deployment Embeddings lightonai Embeddings 1 variants 516 dl/mo

modernbert-embed-large-unsupervised is an open-weight embedding model from lightonai. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released bylightonai
TypeEmbedding models
TaskEmbeddings
Parameters (lead)395M
Context8k tokens
Runs withsentence-transformers
Based onanswerdotai/ModernBERT-large
Released2025-01-13
Popularity516 downloads / month
LicenceOpen weights

About

What modernbert-embed-large-unsupervised is

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.

Read the full model card

Performance

ModelAverage (56)Classification (12)Clustering (11)Pair Classification (3)Reranking (4)Retrieval (15)STS (10)Overall
nomic-embed-text-v1_unsup59.971.242.583.755.048.080.830.7
modernbert-embed-base-unsupervised60.0372.1144.3482.7855.047.0580.3331.2
modernbert-embed-large-unsupervised60.7172.9044.9683.4455.5447.9080.9529.86

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}
}

From the published model card. Full card on the HuggingFace links in the sidebar.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

TaskDatasetMetricScore
ClassificationMTEB AmazonCounterfactualClassification (en)accuracy76.642
ClassificationMTEB AmazonCounterfactualClassification (en)ap39.438
ClassificationMTEB AmazonCounterfactualClassification (en)f170.473
ClassificationMTEB AmazonPolarityClassificationaccuracy91.830
ClassificationMTEB AmazonPolarityClassificationap88.836
ClassificationMTEB AmazonPolarityClassificationf191.825
ClassificationMTEB AmazonReviewsClassification (en)accuracy47.864
ClassificationMTEB AmazonReviewsClassification (en)f147.281
RetrievalMTEB ArguAnamap_at_126.885
RetrievalMTEB ArguAnamap_at_1041.525
RetrievalMTEB ArguAnamap_at_10042.610
RetrievalMTEB ArguAnamap_at_100042.617
RetrievalMTEB ArguAnamap_at_2042.422
RetrievalMTEB ArguAnamap_at_336.143
RetrievalMTEB ArguAnamap_at_539.215
RetrievalMTEB ArguAnamrr_at_127.169
RetrievalMTEB ArguAnamrr_at_1041.659
RetrievalMTEB ArguAnamrr_at_10042.737
RetrievalMTEB ArguAnamrr_at_100042.743
RetrievalMTEB ArguAnamrr_at_2042.549
RetrievalMTEB ArguAnamrr_at_336.202
RetrievalMTEB ArguAnamrr_at_539.342
RetrievalMTEB ArguAnandcg_at_126.885
RetrievalMTEB ArguAnandcg_at_1050.515

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys modernbert-embed-large-unsupervised for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (modernbert-embed-large-unsupervised below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"modernbert-embed-large-unsupervised","input":"text to embed"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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