Omarbm52/Artemis-Embed-v1

Artemis Embed v1

Artemis Embed v1 is an English general-purpose dense embedding model built on answerdotai/ModernBERT-base.

It produces one L2-normalized dense vector per text and is intended for semantic similarity, retrieval, document search, clustering, and downstream classification features.

Architecture

• Backbone: answerdotai/ModernBERT-base
• Pooling: masked mean
• Full embedding dimension: 768
• Matryoshka dimensions: 768 / 512 / 256 / 128
• Normalization: L2 normalization after truncation
• Tested maximum sequence length in the current release: 128 tokens
• Current training recipe: LoRA + contrastive training + hard negatives + Matryoshka learning

Usage

from sentence_transformers import SentenceTransformer
import torch.nn.functional as F
import torch

model = SentenceTransformer("Omarbm52/Artemis-Embed-v1")

texts = [
    "A dog is running in a park.",
    "A puppy runs outside.",
]

emb = model.encode(
    texts,
    convert_to_tensor=True,
    normalize_embeddings=True,
)

# Full 768D embeddings
emb_768 = emb

# Example Matryoshka 256D embeddings.
emb_256 = F.normalize(emb[:, :256], p=2, dim=-1)

When using a truncated Matryoshka dimension, truncate the prefix first and then L2-normalize again.

Internal development results

These numbers come from the project's internal development suite and are not a final held-out MTEB result.

Metric	768D
Retrieval Recall@1	0.8675
Retrieval Recall@5	0.9700
Retrieval MRR	0.909534
STS Spearman	0.345035
Classification accuracy	0.872
Clustering NMI	0.589730
Internal aggregate	0.702423

Matryoshka development results

Dimension	R@1	R@5	MRR	STS Spearman
768	0.8675	0.9700	0.909534	0.345035
512	0.8550	0.9650	0.901461	0.344076
256	0.8275	0.9525	0.883264	0.330863
128	0.8250	0.9450	0.876263	0.329936

Limitations

• English-only v1.
• Current release metrics are internal development metrics.
• Final MTEB English v2 evaluation remains open.
• The raw ModernBERT baseline is not included in the exported development result table.
• Training arms were not all compute-controlled, so the current winner should not be interpreted as evidence that LoRA is inherently better than full fine-tuning.
• Dataset licensing, overlap, and contamination review must be completed before a research-grade final release claim.

Matryoshka note

Truncating from 768 dimensions to a smaller prefix reduces downstream vector storage, bandwidth, similarity-computation cost, and index size. It does not reduce the ModernBERT backbone forward-pass FLOPs.