license: apache-2.0
base_model: nferruz/ProtGPT2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: AmpGPT2
results: []
AmpGPT2
AmpGPT2 is a language model capable of generating de novo antimicrobial peptides (AMPs). Generated sequences are predicted to be AMPs 95.83% of the time.
Model description
AmpGPT2 is a fine-tuned version of nferruz/ProtGPT2 based on the GPT2 Transformer architecture.
| Training Loss | Epoch | Validation Loss | Accuracy |
|---|---|---|---|
| 3.7948 | 50.0 | 3.9890 | 0.4213 |
To validate the results the Antimicrobial Peptide Scanner vr.2 (https://www.dveltri.com/ascan/v2/ascan.html) was used, which is a deep learning tool specifically designed for AMP recognition.
Training and evaluation data
AmpGPT2 was trained using 32014 AMP sequences from the Compass (https://compass.mathematik.uni-marburg.de/) database.
How to use AmpGPT2
The example code below contains the ideal generation settings found while testing. The 'num_return_sequences' parameter specifies the amount of sequences generated. When generating more than 100 sequences at the same time, I recommend doing it in batches. The results can then be checked with the peptide scanner.
from transformers import pipeline
from transformers import GPT2LMHeadModel, GPT2Tokenizer
ampgpt2 = pipeline('text-generation', model="wabu/AmpGPT2")
model_amp = GPT2LMHeadModel.from_pretrained('wabu/AmpGPT2')
tokenizer_amp = GPT2Tokenizer.from_pretrained('wabu/AmpGPT2')
amp_sequences = ampgpt2( "", do_sample=True, repetition_penalty=1.2, num_return_sequences=10, eos_token_id=0 )
for i, seq in enumerate(amp_sequences):
sequence_identifier = f"Sequence_{i + 1}"
sequence = seq['generated_text'].replace('','').strip()
print(f">{sequence_identifier}\n{sequence}")
Training hyperparameters and results
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
\begin{table}[h!] \centering \caption{AMP Yield Comparison between AmpGPT2 and ProtGPT2} \begin{tabular}{lccc} \toprule Model & Total Sequences & AMP Classified & AMP Percentage (AMP%) \ \midrule AmpGPT2 & 10000 & 9541 & 95.41% \ ProtGPT2 & 10000 & 5530 & 55.3% \ \bottomrule \end{tabular} \label{tab:amp_yield} \end{table}
| Model | Amp% | Length |
|---|---|---|
| AmpGPT2 | 95.86 | 64.08 |
| ProtGPT2 | 51.85 | 222.59 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
The model was trained on four NVIDIA A100 GPUs.