metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: output_v3
    results: []
widget:
  - text: <|endoftext|>MAADGYLPDWLEDNLSEGIREWWALKPGAPQPKANQQHQDNARGLVLPGYKYLGPGNGL

output_v3

This model is a fine-tuned version of avuhong/ParvoGPT2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4775
Accuracy: 0.9290

Model description

This model is a GPT2-like model for generating capsid amino acid sequences. It was trained exclusively on capsid aa_seqs of Piccovirales members.

Intended uses & limitations

As a typical GPT model, it can be used to generate new sequences or used to evaluate the perplexity of given sequences.

Generate novel sequences for viral capsid proteins

from transformers import pipeline
protgpt2 = pipeline('text-generation', model="avuhong/PiccoviralesGPT")
sequences = protgpt2("<|endoftext|>", max_length=750, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=10, eos_token_id=0)

Calculate the perplexity of a protein sequence

def calculatePerplexity(sequence, model, tokenizer):
    input_ids = torch.tensor(tokenizer.encode(sequence)).unsqueeze(0) 
    input_ids = input_ids.to(device)
    with torch.no_grad():
        outputs = model(input_ids, labels=input_ids)
    loss, logits = outputs[:2]
    return math.exp(loss)

def split_sequence(sequence):
    chunks = []
    max_i = 0
    for i in range(0, len(sequence), 60):
        chunk = sequence[i:i+60]
        
        if i == 0:
            chunk = '<|endoftext|>' + chunk
        chunks.append(chunk)
        max_i = i
    
    chunks = '\n'.join(chunks)
    
    if max_i+61==len(sequence):
        chunks = chunks+"\n<|endoftext|>"
    else:
        chunks = chunks+"<|endoftext|>"
    return chunks

seq = "MAADGYLPDWLEDNLSEGIREWWALKPGAPQPKANQQHQDNARGLVLPGYKYLGPGNGLDKGEPVNAADAAALEHDKAYDQQLKAGDNPYLKYNHADAEFQERLKEDTSFGGNLGRAVFQAKKRLLEPLGLVEEAAKTAPGKKRPVEQSPQEPDSSAGIGKSGAQPAKKRLNFGQTGDTESVPDPQPIGEPPAAPSGVGSLTMASGGGAPVADNNEGADGVGSSSGNWHCDSQWLGDRVITTSTRTWALPTYNNHLYKQISNSTSGGSSNDNAYFGYSTPWGYFDFNRFHCHFSPRDWQRLINNNWGFRPKRLNFKLFNIQVKEVTDNNGVKTIANNLTSTVQVFTDSDYQLPYVLGSAHEGCLPPFPADVFMIPQYGYLTLNDGSQAVGRSSFYCLEYFPSQMLRTGNNFQFSYEFENVPFHSSYAHSQSLDRLMNPLIDQYLYYLSKTINGSGQNQQTLKFSVAGPSNMAVQGRNYIPGPSYRQQRVSTTVTQNNNSEFAWPGASSWALNGRNSLMNPGPAMASHKEGEDRFFPLSGSLIFGKQGTGRDNVDADKVMITNEEEIKTTNPVATESYGQVATNHQSAQAQAQTGWVQNQGILPGMVWQDRDVYLQGPIWAKIPHTDGNFHPSPLMGGFGMKHPPPQILIKNTPVPADPPTAFNKDKLNSFITQYSTGQVSVEIEWELQKENSKRWNPEIQYTSNYYKSNNVEFAVNTEGVYSEPRPIGTRYLTRNL"
seq = split_sequence(seq)
print(f"{calculatePerplexity(seq, model, tokenizer):.2f}")

Training and evaluation data

Traning script is included in bash file in this repository.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 8
total_eval_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 32.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	220	1.1623	0.8225
No log	2.0	440	0.9566	0.8539
1.1942	3.0	660	0.8456	0.8709
1.1942	4.0	880	0.7719	0.8801
0.7805	5.0	1100	0.7224	0.8872
0.7805	6.0	1320	0.6895	0.8928
0.6257	7.0	1540	0.6574	0.8972
0.6257	8.0	1760	0.6289	0.9014
0.6257	9.0	1980	0.6054	0.9045
0.5385	10.0	2200	0.5881	0.9077
0.5385	11.0	2420	0.5709	0.9102
0.4778	12.0	2640	0.5591	0.9121
0.4778	13.0	2860	0.5497	0.9143
0.427	14.0	3080	0.5385	0.9161
0.427	15.0	3300	0.5258	0.9180
0.394	16.0	3520	0.5170	0.9195
0.394	17.0	3740	0.5157	0.9212
0.394	18.0	3960	0.5038	0.9221
0.363	19.0	4180	0.4977	0.9234
0.363	20.0	4400	0.4976	0.9236
0.3392	21.0	4620	0.4924	0.9247
0.3392	22.0	4840	0.4888	0.9255
0.33	23.0	5060	0.4890	0.9262
0.33	24.0	5280	0.4856	0.9268
0.3058	25.0	5500	0.4803	0.9275
0.3058	26.0	5720	0.4785	0.9277
0.3058	27.0	5940	0.4813	0.9281
0.2973	28.0	6160	0.4799	0.9282
0.2973	29.0	6380	0.4773	0.9285
0.2931	30.0	6600	0.4778	0.9286
0.2931	31.0	6820	0.4756	0.9290
0.2879	32.0	7040	0.4775	0.9290

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2