BacteriaCDS-DNABERT-K6-89M

BacteriaCDS-DNABERT-K6-89M

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)

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Install Dependencies

Ensure you have transformers and torch installed:

pip install torch transformers

Load Model & Tokenizer

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:

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()

<!-- ### **Citation** If you use this model in your research, please cite: ```tex @article{paper title, title={DNABERT for Bacterial CDS Classification}, author={Genereux Akotenou, et al.}, journal={Hugging Face Model Hub}, year={2024} } ``` -->