BioLinkBERT-Large-LitCovid-1.4 – AI Model by sofia-todeschini | AlphaNeural AI

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sofia-todeschini/
BioLinkBERT-Large-LitCovid-1.4

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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BioLinkBERT-Large-LitCovid-1.4

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

Loss: 0.5976
Hamming loss: 0.0604
F1 micro: 0.6804
F1 macro: 0.5425
F1 weighted: 0.7357
F1 samples: 0.6807
Precision micro: 0.5509
Precision macro: 0.4271
Precision weighted: 0.6552
Precision samples: 0.5921
Recall micro: 0.8895
Recall macro: 0.8221
Recall weighted: 0.8895
Recall samples: 0.9063
Roc Auc: 0.9165
Accuracy: 0.1370

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Hamming loss	F1 micro	F1 macro	F1 weighted	F1 samples	Precision micro	Precision macro	Precision weighted	Precision samples	Recall micro	Recall macro	Recall weighted	Recall samples	Roc Auc	Accuracy
0.5869	1.0	1151	0.5737	0.0978	0.5682	0.4375	0.6759	0.5754	0.4172	0.3269	0.5906	0.4591	0.8901	0.8593	0.8901	0.9076	0.8966	0.0421
0.4636	2.0	2302	0.5316	0.0805	0.6179	0.4702	0.7052	0.6237	0.4704	0.3554	0.6181	0.5153	0.9005	0.8611	0.9005	0.9160	0.9107	0.0812
0.3782	3.0	3453	0.5382	0.0760	0.6321	0.4929	0.7146	0.6327	0.4864	0.3757	0.6293	0.5230	0.9027	0.8556	0.9027	0.9183	0.9142	0.0797
0.3031	4.0	4605	0.5807	0.0619	0.6754	0.5346	0.7343	0.6744	0.5437	0.4189	0.6531	0.5820	0.8915	0.8274	0.8915	0.9089	0.9166	0.1235
0.2625	5.0	5755	0.5976	0.0604	0.6804	0.5425	0.7357	0.6807	0.5509	0.4271	0.6552	0.5921	0.8895	0.8221	0.8895	0.9063	0.9165	0.1370

Framework versions

Transformers 4.28.0
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.13.3