This model is a fine-tuned version of
Mistral for
stress region prediction in DNA sequences.
It was developed by the iGEM UGM-Indonesia team as part of the 2025 iGEM project.
The Mistral-Stress-Predictor is designed to classify DNA sequences into stress and non-stress regions.
This model leverages the Mistral architecture and has been fine-tuned on a custom dataset curated by the iGEM UGM team.
You can use this model for DNA stress prediction tasks with Hugging Face Transformers.
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4tokenizer = AutoTokenizer.from_pretrained("igemugm/mistral-athaliana-stress-predictor", trust_remote_code=True)
5model = AutoModelForSequenceClassification.from_pretrained("igemugm/mistral-athaliana-stress-predictor", trust_remote_code=True)
6
7sequence = "ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC"
8inputs = tokenizer(sequence, return_tensors="pt")
9
10if 'token_type_ids' in inputs:
11 inputs.pop('token_type_ids')
12
13with torch.no_grad():
14 outputs = model(**inputs)
15 predictions = torch.softmax(outputs.logits, dim=-1)
16 predicted_class = torch.argmax(predictions, dim=1).item()
17
18print("Predicted class:", predicted_class)
19print("Confidence scores:", predictions)
