libCap_prBERTbfd_clf – AI Model by avuhong | AlphaNeural AI

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avuhong/
libCap_prBERTbfd_clf

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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libCap_prBERTbfd_clf

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

Loss: 0.5197
Accuracy: 0.7457
F1: 0.7940
Precision: 0.7567
Recall: 0.8352
Auroc: 0.7268

Model description

More information needed

Intended uses & limitations

More information needed

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 64
total_train_batch_size: 4096
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Auroc
No log	0.98	34	0.6393	0.6396	0.7053	0.6782	0.7345	0.6197
No log	1.98	68	0.5713	0.6962	0.7499	0.7256	0.7759	0.6795
No log	2.98	102	0.5652	0.7126	0.7388	0.7918	0.6924	0.7168
No log	3.98	136	0.5360	0.7330	0.7896	0.7345	0.8536	0.7076
No log	4.98	170	0.5223	0.7423	0.7830	0.7740	0.7921	0.7318
No log	5.98	204	0.5180	0.7454	0.7882	0.7699	0.8075	0.7323
No log	6.98	238	0.5179	0.7440	0.7934	0.7537	0.8376	0.7243
No log	7.98	272	0.5197	0.7457	0.7940	0.7567	0.8352	0.7268

Framework versions

Transformers 4.21.1
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1