README.md

metadata

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. 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