Model reference · open weights
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 by | lightonai |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 395M |
| Context | 8k tokens |
| Runs with | sentence-transformers |
| Based on | answerdotai/ModernBERT-large |
| Released | 2025-01-13 |
| Popularity | 516 downloads / month |
| Licence | Open weights |
About
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.
| 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 |
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.
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
As published on the model card — the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| Classification | MTEB AmazonCounterfactualClassification (en) | accuracy | 76.642 |
| Classification | MTEB AmazonCounterfactualClassification (en) | ap | 39.438 |
| Classification | MTEB AmazonCounterfactualClassification (en) | f1 | 70.473 |
| Classification | MTEB AmazonPolarityClassification | accuracy | 91.830 |
| Classification | MTEB AmazonPolarityClassification | ap | 88.836 |
| Classification | MTEB AmazonPolarityClassification | f1 | 91.825 |
| Classification | MTEB AmazonReviewsClassification (en) | accuracy | 47.864 |
| Classification | MTEB AmazonReviewsClassification (en) | f1 | 47.281 |
| Retrieval | MTEB ArguAna | map_at_1 | 26.885 |
| Retrieval | MTEB ArguAna | map_at_10 | 41.525 |
| Retrieval | MTEB ArguAna | map_at_100 | 42.610 |
| Retrieval | MTEB ArguAna | map_at_1000 | 42.617 |
| Retrieval | MTEB ArguAna | map_at_20 | 42.422 |
| Retrieval | MTEB ArguAna | map_at_3 | 36.143 |
| Retrieval | MTEB ArguAna | map_at_5 | 39.215 |
| Retrieval | MTEB ArguAna | mrr_at_1 | 27.169 |
| Retrieval | MTEB ArguAna | mrr_at_10 | 41.659 |
| Retrieval | MTEB ArguAna | mrr_at_100 | 42.737 |
| Retrieval | MTEB ArguAna | mrr_at_1000 | 42.743 |
| Retrieval | MTEB ArguAna | mrr_at_20 | 42.549 |
| Retrieval | MTEB ArguAna | mrr_at_3 | 36.202 |
| Retrieval | MTEB ArguAna | mrr_at_5 | 39.342 |
| Retrieval | MTEB ArguAna | ndcg_at_1 | 26.885 |
| Retrieval | MTEB ArguAna | ndcg_at_10 | 50.515 |
Using it via the API
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.