BidirLM is a family of 5 frontier bidirectional encoders, including an omnimodal variant at 2.5B, adapted from causal decoder LLMs. Contrary to contrastive-only models, BidirLM relies on a prior masking phase (MNTP) that enables state-of-the-art results on task-specific fine-tuning (NER, classification, NLI) while achieving frontier performance on embedding benchmarks (MTEB) against open-source alternatives.

Multilingual model performance by size on XTREME-Benchmark Augmented and MTEB Multilingual V2

From the paper. MTEB(Multilingual, v2) below matches the paper; the LongEmbed table reports additional long-context results.

Model	Base LLM	Parameters	Embedding Dim	Max Tokens	MTEB Multi. V2 (Mean Task)
BidirLM-270M	Gemma3-270M	268M	640	512	56.3
BidirLM-0.6B	Qwen3-0.6B	596M	1024	512	60.0
BidirLM-1B	Gemma3-1B	1001M	1152	512 (*)	62.7
BidirLM-1.7B	Qwen3-1.7B	1721M	2048	512	63.1
BidirLM-Omni-2.5B	Qwen3-1.7B	2.5B	2048	512	63.1

(*) Evaluated at max_seq_length=512, matching the paper. The architecture supports much longer context (see the LongEmbed table below); on the MTEB leaderboard the shared LEMBPasskeyRetrieval task is scored at long context, making the leaderboard MTEB(Multilingual, v2) ~0.4 higher than this table.

LongEmbed (Long-Context Retrieval)

Mean nDCG@10 over the 6 LongEmbed retrieval tasks. Each model is reported at the context length (8k or 32k) that maximizes its average; the architecture supports the base model's full context.

Model	LongEmbed (Mean nDCG@10)	Eval Context
BidirLM-270M	71.8	32k
BidirLM-0.6B	71.9	8k
BidirLM-1B	76.4	32k
BidirLM-1.7B	73.4	8k

Note: Extending the evaluation context from 8k to 32k helped the Gemma-based models (270M, 1B) but not the Qwen-based models (0.6B, 1.7B), which scored best at 8k.

Supported Tasks

General embeddings (via Sentence Transformers): retrieval, semantic similarity (STS), clustering, classification, pair classification, reranking, bitext mining, multilabel classification

Downstream fine-tuning (via Transformers): sequence classification (e.g. MNLI, XNLI, PAWS-X, MathShepherd), token classification (e.g. PAN-X, POS), information retrieval (e.g. MIRACL, CodeSearchNet), sequence regression (e.g. Seahorse)

Usage

Sentence Transformers

Use Sentence Transformers to compute embeddings for any text representation task.

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("BidirLM/BidirLM-1B-Embedding", trust_remote_code=True)

queries = [
    "What is the capital of France?",
    "How does photosynthesis work?",
]
documents = [
    "Paris is the capital and largest city of France, situated on the river Seine.",
    "Photosynthesis is the process by which plants convert sunlight, water, and CO2 into glucose and oxygen.",
]

query_embeddings = model.encode(queries)
document_embeddings = model.encode(documents)

similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)

Fine-tuning for Downstream Tasks

BidirLM can be directly fine-tuned for downstream tasks:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("BidirLM/BidirLM-1B-Embedding", trust_remote_code=True)

# Sequence classification (e.g., NLI: entailment, neutral, contradiction)
seq_model = AutoModelForSequenceClassification.from_pretrained(
    "BidirLM/BidirLM-1B-Embedding",
    trust_remote_code=True,
    num_labels=3,
)

# Token classification (e.g., NER)
tok_model = AutoModelForTokenClassification.from_pretrained(
    "BidirLM/BidirLM-1B-Embedding",
    trust_remote_code=True,
    num_labels=7,
)

# Fine-tune with HuggingFace Trainer

Evaluation

Please follow the mteb repository on how to reproduce our scores. The evaluation prompts used for each task are also available at mteb_v2_eval_prompts.json.

Supported Languages

Multilingual support across over 140 languages, inherited from the Gemma3 base model and reinforced through contrastive training with 87 languages.

Requirements

This model requires trust_remote_code=True as it uses a custom bidirectional architecture.

transformers>=5.0
sentence-transformers>=5.0.0

Note: This model was trained with transformers==4.57.6 (transformers 4.x). The version on main was patched to work with transformers>=5.0. For the original (pre-patch) version, which is compatible with transformers>=4.57.6,<5.0.0, use the transformers-v4 branch:

python
1from sentence_transformers import SentenceTransformer 2model = SentenceTransformer( 3 "BidirLM/BidirLM-1B-Embedding", 4 trust_remote_code=True, 5 revision="transformers-v4", 6)

FAQ

1. What pooling strategy does this model use?

The model uses mean pooling. This is handled automatically when using Sentence Transformers.

2. Do I need `trust_remote_code=True`?

Yes. BidirLM uses a custom bidirectional architecture (BidirLMModel) that requires loading custom code from the repository.

3. Why are my reproduced results slightly different from those reported in the model card?

Different versions of transformers and pytorch could cause negligible but non-zero performance differences. This model should be used with transformers>=5.0 (evaluated with transformers==5.5.4 and pytorch==2.6.0).

4. What is the relationship between BidirLM-1B and BidirLM-1B-Base?

BidirLM/BidirLM-1B-Base is the intermediate MNTP-adapted checkpoint (bidirectional pretraining stage). BidirLM-1B is the final contrastive fine-tuned version optimized for both sentence embeddings and downstream fine-tuning.

5. How is BidirLM different from other embedding models?

Most embedding models (BGE-M3, KaLM, EmbedGemma, Qwen3-Embedding) use contrastive-only training, which optimizes embeddings but sacrifices fine-tuning ability. BidirLM restores a prior MNTP phase, advancing the Pareto frontier on both MTEB and XTREME simultaneously.

Citation

bibtex

@misc{boizard2026bidirlmtextomnimodalbidirectional,
      title={BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs}, 
      author={Nicolas Boizard and Théo Deschamps-Berger and Hippolyte Gisserot-Boukhlef and Céline Hudelot and Pierre Colombo},
      year={2026},
      eprint={2604.02045},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.02045}, 
}

BidirLM/BidirLM-1B-Embedding

BidirLM-1B