---
license: apache-2.0
base_model: nferruz/ProtGPT2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: AmpGPT2
  results: []
---

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# 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](https://huggingface.co/nferruz/ProtGPT2) based on the GPT2 Transformer architecture. 
To validate the results the Antimicrobial Peptide Scanner vr.2 (https://www.dveltri.com/ascan/v2/ascan.html) was used. It is a 

## 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 
```
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

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

### Training results
these are the training losses after the final epoch

| Training Loss | Epoch | Validation Loss | Accuracy |
|:-------------:|:-----:|:---------------:|:--------:|
| 3.7948        | 50.0  | 3.9890          | 0.4213   |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0