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