bert-wellness-classifier_teacher – AI Model by hebashakeel | AlphaNeural AI

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hebashakeel/
bert-wellness-classifier_teacher

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-wellness-classifier_teacher

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

Loss: 0.7694
Accuracy: 0.688
Auc: 0.9
Precision Class 0: 0.862
Precision Class 1: 0.739
Precision Class 2: 0.613
Precision Class 3: 0.538
Recall Class 0: 0.781
Recall Class 1: 0.81
Recall Class 2: 0.704
Recall Class 3: 0.483

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: 0.0002
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	Accuracy	Auc	Precision Class 0	Precision Class 1	Precision Class 2	Precision Class 3	Recall Class 0	Recall Class 1	Recall Class 2	Recall Class 3
1.3247	1.0	63	1.1856	0.569	0.819	0.759	0.727	0.414	0.727	0.688	0.381	0.889	0.276
1.0767	2.0	126	1.0166	0.587	0.857	0.667	0.643	1.0	0.462	0.75	0.429	0.259	0.828
0.9285	3.0	189	0.9616	0.633	0.875	0.846	0.486	1.0	0.528	0.688	0.857	0.37	0.655
0.8628	4.0	252	0.8910	0.624	0.885	0.88	0.514	0.64	0.5	0.688	0.857	0.593	0.414
0.7828	5.0	315	0.8369	0.679	0.888	0.88	0.667	0.75	0.514	0.688	0.857	0.556	0.655
0.7489	6.0	378	0.7962	0.706	0.899	0.857	0.762	0.704	0.545	0.75	0.762	0.704	0.621
0.6981	7.0	441	0.8118	0.679	0.896	0.88	0.708	0.633	0.533	0.688	0.81	0.704	0.552
0.6634	8.0	504	0.7915	0.688	0.898	0.889	0.708	0.655	0.517	0.75	0.81	0.704	0.517
0.6651	9.0	567	0.7777	0.67	0.9	0.862	0.739	0.576	0.5	0.781	0.81	0.704	0.414
0.6591	10.0	630	0.7694	0.688	0.9	0.862	0.739	0.613	0.538	0.781	0.81	0.704	0.483

Framework versions

Transformers 4.45.1
Pytorch 2.4.0+cpu
Datasets 3.0.1
Tokenizers 0.20.0