biobert-all – AI Model by jialinselenasong | AlphaNeural AI

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jialinselenasong/
biobert-all

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token-classification

generated_from_trainer

dmis-lab/biobert-v1.1

autotrain_compatible

endpoints_compatible

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biobert-all

This model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7750
Precision: 0.5990
Recall: 0.6572
F1: 0.6268
Accuracy: 0.8385

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	363	0.4337	0.5819	0.6535	0.6156	0.8427
0.4325	2.0	726	0.4422	0.5912	0.6675	0.6270	0.8438
0.2832	3.0	1089	0.4720	0.6010	0.6422	0.6209	0.8443
0.2832	4.0	1452	0.5342	0.6076	0.6522	0.6291	0.8454
0.1948	5.0	1815	0.5969	0.6059	0.6594	0.6315	0.8415
0.1315	6.0	2178	0.6428	0.6051	0.6551	0.6291	0.8408
0.0987	7.0	2541	0.6933	0.5933	0.6649	0.6270	0.8384
0.0987	8.0	2904	0.7353	0.5949	0.6633	0.6273	0.8390
0.0762	9.0	3267	0.7640	0.5920	0.6623	0.6252	0.8389
0.0628	10.0	3630	0.7750	0.5990	0.6572	0.6268	0.8385

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1