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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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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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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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To validate the results the Antimicrobial Peptide Scanner vr.2 (https://www.dveltri.com/ascan/v2/ascan.html) was used. It is a |
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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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``` |
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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}") |
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``` |
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### Training hyperparameters |
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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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### Training results |
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these are the training losses after the final epoch |
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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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### 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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