Bert-Thesis-NonKFold – AI Model by jmLuis | AlphaNeural AI

You can deploy this model and start earning money today!

jmLuis/
Bert-Thesis-NonKFold

like

0

text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

Views

No views yet

Bert-Thesis-NonKFold

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

Loss: 1.4861
F1: 0.7464
Recall: 0.7464
Accuracy: 0.7464

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: 32
eval_batch_size: 32
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	F1	Recall	Accuracy
1.0891	1.0	1446	0.9053	0.7222	0.7222	0.7222
0.7239	2.0	2892	0.8697	0.7397	0.7397	0.7397
0.4902	3.0	4338	0.8814	0.7491	0.7491	0.7491
0.3287	4.0	5784	0.9655	0.7512	0.7512	0.7512
0.2156	5.0	7230	1.0648	0.7450	0.7450	0.7450
0.1473	6.0	8676	1.1826	0.7446	0.7446	0.7446
0.1071	7.0	10122	1.2922	0.7465	0.7465	0.7465
0.0692	8.0	11568	1.4034	0.7483	0.7483	0.7483
0.0511	9.0	13014	1.4611	0.7478	0.7478	0.7478
0.0386	10.0	14460	1.4861	0.7464	0.7464	0.7464

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3