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

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sofia-todeschini/
BioLinkBERT-LitCovid-1.4

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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BioLinkBERT-LitCovid-1.4

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

Loss: 0.5613
Hamming loss: 0.0775
F1 micro: 0.6253
F1 macro: 0.4797
F1 weighted: 0.7043
F1 samples: 0.6321
Precision micro: 0.4806
Precision macro: 0.3631
Precision weighted: 0.6169
Precision samples: 0.5276
Recall micro: 0.8947
Recall macro: 0.8442
Recall weighted: 0.8947
Recall samples: 0.9099
Roc Auc: 0.9097
Accuracy: 0.0849

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
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
0.6654	1.0	1151	0.6313	0.1143	0.5259	0.3963	0.6460	0.5359	0.3756	0.2909	0.5586	0.4182	0.8764	0.8497	0.8764	0.8940	0.8814	0.0227
0.5313	2.0	2303	0.5682	0.0997	0.5655	0.4266	0.6717	0.5784	0.4128	0.3161	0.5789	0.4624	0.8972	0.8620	0.8972	0.9120	0.8988	0.0492
0.4594	3.0	3454	0.5529	0.0884	0.5938	0.4517	0.6907	0.6012	0.4446	0.3394	0.6041	0.4883	0.8939	0.8549	0.8939	0.9094	0.9034	0.0586
0.3966	4.0	4606	0.5580	0.0797	0.6193	0.4739	0.7014	0.6245	0.4731	0.3579	0.6129	0.5166	0.8965	0.8476	0.8965	0.9109	0.9093	0.0751
0.3693	5.0	5755	0.5613	0.0775	0.6253	0.4797	0.7043	0.6321	0.4806	0.3631	0.6169	0.5276	0.8947	0.8442	0.8947	0.9099	0.9097	0.0849

Framework versions

Transformers 4.28.0
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.13.3