clinicalAgitationTextClassificationModelV1 – AI Model by Dinithi | AlphaNeural AI

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Dinithi/
clinicalAgitationTextClassificationModelV1

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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clinicalAgitationTextClassificationModelV1

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

Loss: 0.7664
Accuracy: 0.86
Precision: 0.8986
Recall: 0.8986
F1: 0.8986

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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6593	1.0	50	0.5648	0.74	0.8525	0.7536	0.8
0.5082	2.0	100	0.4731	0.76	0.8814	0.7536	0.8125
0.3744	3.0	150	0.4516	0.84	0.8732	0.8986	0.8857
0.2618	4.0	200	0.5435	0.83	0.8514	0.9130	0.8811
0.2145	5.0	250	0.4750	0.85	0.95	0.8261	0.8837
0.1426	6.0	300	0.5433	0.86	0.8986	0.8986	0.8986
0.0971	7.0	350	0.5997	0.85	0.9355	0.8406	0.8855
0.0741	8.0	400	0.7534	0.86	0.8986	0.8986	0.8986
0.0513	9.0	450	0.8293	0.83	0.8611	0.8986	0.8794
0.0456	10.0	500	0.7664	0.86	0.8986	0.8986	0.8986

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1