This model, BacteriaCDS-DNABERT-K6-89M, is a DNA sequence classifier based on DNABERT trained for coding sequence (CDS) classification in bacterial genomes. It operates on 6-mer tokenized sequences and was fine-tuned using 89M trainable parameters.

Model Details

Base Model: DNABERT
Task: Bacterial CDS Classification
K-mer Size: 6
Input Sequence: Open Reading Frame(Last 510 nucleotides from end of the sequence)
Number of Trainable Parameters: 89M
Max Sequence Length: 512
Precision Used: AMP (Automatic Mixed Precision)

Install Dependencies

Ensure you have transformers and torch installed:

pip install torch transformers

Load Model & Tokenizer

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load Model
model_checkpoint = "Genereux-akotenou/BacteriaCDS-DNABERT-K6-89M"
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

Inference Example

This model works with 6-mer tokenized sequences. You need to convert raw DNA sequences into k-mer format:

python

def generate_kmer(sequence: str, k: int, overlap: int = 1):
    return " ".join([sequence[j:j+k] for j in range(0, len(sequence) - k + 1, overlap)])

sequence = "ATGAGAACCAGCCGGAGACCTCCTGCTCGTACATGAAAGGCTCGAGCAGCCGGGCGAGGGCGGTAG" 
seq_kmer = generate_kmer(sequence, k=6, overlap=3)

# Run inference
inputs = tokenizer(
  seq_kmer,
  return_tensors="pt",
  max_length=tokenizer.model_max_length,
  padding="max_length",
  truncation=True
)
with torch.no_grad():
  outputs = model(**inputs)
  logits = outputs.logits
  predicted_class = torch.argmax(logits, dim=-1).item()