---
language:
- dna
library_name: transformers
tags:
- DNA
- BERT
- language-model
- genomics
license: apache-2.0
---

# DNABERT-S

Weights and tokenizer for [DNABERT-S](https://arxiv.org/abs/2402.08777)
(Zhou et al., arXiv 2024), loaded with the shared MosaicBERT implementation
from [Taykhoom/MosaicBERT-updated](https://huggingface.co/Taykhoom/MosaicBERT-updated).

DNABERT-S is a species-aware DNA embedding model fine-tuned from DNABERT-2 using
curriculum contrastive learning. It generates embeddings that naturally cluster and
segregate genomes from different species, enabling species identification,
metagenomics binning, and evolutionary analysis.

**This repo contains only weights and tokenizer files.** The model code is loaded
automatically from `Taykhoom/MosaicBERT-updated` via `trust_remote_code=True`.

## Architecture

| Parameter | Value |
|---|---|
| Layers | 12 |
| Attention heads | 12 |
| Embedding dimension | 768 |
| Intermediate size | 3072 |
| Vocabulary size | 4096 (BPE, identical to DNABERT-2) |
| Positional encoding | ALiBi (no hard length limit) |
| Max sequence length | ~10000 nt (practical; ALiBi resizes dynamically) |
| Parameters | ~110M (backbone only, no MLM head) |

### Tokenization

Uses Byte Pair Encoding (BPE) tokenization via `PreTrainedTokenizerFast`,
identical vocabulary to DNABERT-2. No k-mer pre-processing required.

## Pretraining

- **Objective:** Curriculum contrastive learning (same-species pairs with i-Mix)
- **Initialization:** Fine-tuned from [zhihan1996/DNABERT-2-117M](https://huggingface.co/zhihan1996/DNABERT-2-117M)
- **Source checkpoint:** `pytorch_model.bin` from [zhihan1996/DNABERT-S](https://huggingface.co/zhihan1996/DNABERT-S)

## Parity Verification

Hidden-state representations verified identical (max abs diff = 0.00) to the original
implementation at all 13 representation levels (embedding + 12 transformer layers).
SDPA verified (max abs diff < 1e-4). Verified on GPU with PyTorch 2.7 / CUDA 12.9.

## Related Models

See the full [DNABERT collection](https://huggingface.co/collections/Taykhoom/dnabert-6a20958f8ce004ea4e985e7b).

| Model | Architecture | Notes |
|---|---|---|
| [DNABERT-3mer](https://huggingface.co/Taykhoom/DNABERT-3mer) | BERT + k-mer | k=3 |
| [DNABERT-4mer](https://huggingface.co/Taykhoom/DNABERT-4mer) | BERT + k-mer | k=4 |
| [DNABERT-5mer](https://huggingface.co/Taykhoom/DNABERT-5mer) | BERT + k-mer | k=5 |
| [DNABERT-6mer](https://huggingface.co/Taykhoom/DNABERT-6mer) | BERT + k-mer | k=6 |
| [DNABERT-2](https://huggingface.co/Taykhoom/DNABERT2) | MosaicBERT + BPE + ALiBi | Pre-trained |
| **[DNABERT-S](https://huggingface.co/Taykhoom/DNABERT-S)** | **MosaicBERT + BPE + ALiBi** | **This model** |

## Usage

### Embedding generation

```python
import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Taykhoom/DNABERT-S", trust_remote_code=True)
model = AutoModel.from_pretrained("Taykhoom/DNABERT-S", trust_remote_code=True)
model.eval()

sequences = ["ACGTAGCATCGGATCTATCTATCGACACTTGG", "ATCGATCGATCGATCG"]
enc = tokenizer(sequences, return_tensors="pt", padding=True)

with torch.no_grad():
    out = model(**enc)

cls_emb  = out.last_hidden_state[:, 0, :]   # (batch, 768)
mean_emb = out.last_hidden_state.mean(dim=1) # (batch, 768) -- mean pooling
```

### Attention implementation

```python
# SDPA (default on PyTorch >= 2.0)
model = AutoModel.from_pretrained("Taykhoom/DNABERT-S", trust_remote_code=True,
                                   attn_implementation="sdpa")

# Flash Attention 2
model = AutoModel.from_pretrained("Taykhoom/DNABERT-S", trust_remote_code=True,
                                   attn_implementation="flash_attention_2",
                                   torch_dtype=torch.bfloat16)
```

## Implementation Notes

The original DNABERT-S codebase uses a Triton-based flash attention implementation
(`flash_attn_triton.py`). This HF port uses
[Taykhoom/MosaicBERT-updated](https://huggingface.co/Taykhoom/MosaicBERT-updated)
which replaces it with the standard `flash-attn` package, and also adds
`attn_implementation="sdpa"` support. These were not part of the original codebase.

## Citation

```bibtex
@misc{zhou2024_dnaberts,
  title   = {{DNABERT}-S: Learning Species-Aware {DNA} Embedding with Genome Foundation Models},
  author  = {Zhou, Zhihan and Wu, Winmin and Ho, Harrison and Wang, Jiayi and
             Shi, Lizhen and Davuluri, Ramana V and Wang, Zhong and Liu, Han},
  year    = {2024},
  eprint  = {2402.08777},
  archivePrefix = {arXiv},
  primaryClass  = {q-bio.GN}
}
```

## Credits

Original DNABERT-S model and code by Zhou et al.
Source: [GitHub](https://github.com/MAGICS-LAB/DNABERT_S).
The HF conversion code was authored primarily by [Claude Code](https://claude.ai/code)
and reviewed manually by Taykhoom Dalal.

## License

Apache 2.0, following the original repository.