BioLinkBERT-LitCovid-v1.2.3 – AI Model by sofia-todeschini | AlphaNeural AI

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sofia-todeschini/
BioLinkBERT-LitCovid-v1.2.3

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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BioLinkBERT-LitCovid-v1.2.3

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: 0.9691
F1 micro: 0.8266
F1 macro: 0.3107
F1 weighted: 0.8821
F1 samples: 0.8868
Precision micro: 0.7335
Precision macro: 0.2518
Precision weighted: 0.8347
Precision samples: 0.8699
Recall micro: 0.9468
Recall macro: 0.7764
Recall weighted: 0.9468
Recall samples: 0.9538
Roc Auc: 0.9640
Accuracy: 0.7104

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation 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
1.1059	1.0	2272	0.7606	0.7517	0.2617	0.8428	0.8566	0.6257	0.2096	0.7778	0.8302	0.9412	0.7947	0.9412	0.9501	0.9553	0.6325
0.6408	2.0	4544	0.8639	0.8057	0.2965	0.8751	0.8786	0.7057	0.2399	0.8315	0.8626	0.9389	0.8070	0.9389	0.9484	0.9588	0.6961
0.6275	3.0	6816	0.9691	0.8266	0.3107	0.8821	0.8868	0.7335	0.2518	0.8347	0.8699	0.9468	0.7764	0.9468	0.9538	0.9640	0.7104

Framework versions

Transformers 4.28.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3