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
More information needed
Intended uses & limitations
Generate novel sequences for viral capsid proteins
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[:-1]
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
More information needed
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