Model Card for Model ID

Model Details

Model Description

This model is designed for genomic sequence classification and generation. It can be directly used with AutoModelForCausalLM or AutoModelForSequenceClassification from the Hugging Face Transformers library. The model is trained on DNA sequences and can perform tasks such as predicting functional genomic elements, classifying sequences, and generating synthetic DNA sequences.

Developed by: [Your Name or Organization]
Funded by [optional]: [Funding Source]
Shared by [optional]: [Your Name or Organization]
Model type: Transformer-based language model for genomic sequence processing
Language(s) (NLP): Not applicable (Genomic sequences: ACGT-based input)
License: MIT
Finetuned from model [optional]: [Pretrained Model Name]

Model Sources [optional]

Repository: [GitHub or Hugging Face Repo]
Paper [optional]: [Link to related paper]
Demo [optional]: [Link to model demo]

Uses

Direct Use

The model can be used for:

DNA sequence classification (e.g., promoter vs. non-promoter classification)
Functional annotation of genomic sequences
Sequence generation for synthetic biology applications

Downstream Use [optional]

Fine-tuned for specific genomic datasets
Integrated into bioinformatics pipelines

Out-of-Scope Use

Not intended for clinical diagnosis or medical decision-making
May not generalize well to non-DNA sequence data

Bias, Risks, and Limitations

Biases: The model may be biased toward training data and may not generalize to all genomic contexts.
Risks: Incorrect classification could mislead downstream biological research.
Limitations: The model does not incorporate structural or epigenetic modifications.

Recommendations

Users should validate predictions using experimental or established computational methods before applying results in critical applications.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your_model_name")
model = AutoModelForCausalLM.from_pretrained("your_model_name")

input_text = "ACGTACGTACGT"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

For classification:

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("your_model_name")

Training Details

Training Data

Dataset: [Provide dataset details or link]
Preprocessing: Tokenization of DNA sequences into k-mers

Training Procedure

Preprocessing: Tokenization using k-mer encoding
Training regime: Mixed precision (fp16 or bf16)

Training Hyperparameters

Learning rate: [Specify]
Batch size: [Specify]
Epochs: [Specify]

Speeds, Sizes, Times [optional]

Training time: [Specify]
Model size: [Specify]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[Provide dataset details]

Factors

GC content, sequence length, species-specific variations

Metrics

Accuracy, precision, recall, F1-score for classification tasks
Perplexity for generation tasks

Results

[Provide evaluation results]

Summary

The model achieves [X]% accuracy on classification and [Y] perplexity on generation tasks.

Model Examination [optional]

Attention visualization tools can be used to interpret sequence importance.

Environmental Impact

Hardware Type: GPUs (A100, V100, or TPU)
Hours used: [Specify]
Cloud Provider: [Specify]
Compute Region: [Specify]
Carbon Emitted: Estimated using Lacoste et al. (2019)

Technical Specifications [optional]

Model Architecture and Objective

Transformer-based model trained for genomic sequence classification and generation

Compute Infrastructure

Hardware

GPUs (A100, V100, or TPU)

Software

Hugging Face Transformers, PyTorch/TensorFlow

Citation [optional]

If you use this model in your research, please cite:

@article{yourcitation,
  title={Your Paper Title},
  author={Your Name and Others},
  journal={Your Journal},
  year={202X}
}

Glossary [optional]

k-mers: Short subsequences of length k used for tokenizing DNA sequences.

More Information [optional]

For further inquiries, contact [your email].

Model Card Authors [optional]

[Your Name] (Your Organization)

Model Card Contact

For support, contact [your email].

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