AgitationNotesClassification – AI Model by Dinithi | AlphaNeural AI | AlphaNeural AI

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Dinithi/
AgitationNotesClassification

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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AgitationNotesClassification

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: 0.4128
F1: 0.8690

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: 1e-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	F1
0.6567	1.0	50	0.6130	0.6500
0.6056	2.0	100	0.5807	0.6763
0.5172	3.0	150	0.5398	0.6675
0.4206	4.0	200	0.4111	0.8355
0.3361	5.0	250	0.3977	0.8667
0.2919	6.0	300	0.3874	0.8780
0.2233	7.0	350	0.3928	0.8690
0.1953	8.0	400	0.3908	0.8690
0.1633	9.0	450	0.4076	0.86
0.1494	10.0	500	0.4128	0.8690

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1