DNABERT-2-117M fish20 adapted
This checkpoint is DNABERT-2 (117M parameters) adapted to the FishNALM 20-species fish-genome corpus for masked language modeling.
Domain-adaptive pretraining
| Setting | Value |
|---|---|
| Base model | DNABERT-2 (117M parameters) |
| Tokenization | BPE |
| Hardware | 4 × NVIDIA A100 80GB |
| Per-device batch size | 64 |
| Gradient accumulation steps | 1 |
| Effective global batch size | 256 |
| Peak learning rate | 2 × 10⁻⁵ |
| Corpus exposure | 1 epoch |
Usage
This repository contains custom model code. Load it with trust_remote_code=True.
from transformers import AutoModelForMaskedLM, AutoTokenizer
model_id = "<your-namespace>/DNABERT-2-117M-fish20-adapted"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True)
The checkpoint is intended for DNA-sequence representation and masked-token prediction. Evaluate and adapt it for each downstream task before use.