Bert-RAdam-XL – AI Model by benchaffe | AlphaNeural AI

You can deploy this model and start earning money today!

benchaffe/
Bert-RAdam-XL

like

0

token-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

Views

No views yet

Bert-RAdam-XL

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1916
Precision: 0.8057
Recall: 0.8640
F1: 0.8338
Accuracy: 0.9455

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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1843	1.0	1000	0.1410	0.7841	0.8672	0.8236	0.9436
0.1156	2.0	2000	0.1443	0.7981	0.8372	0.8172	0.9438
0.0862	3.0	3000	0.1562	0.7947	0.8961	0.8424	0.9477
0.0612	4.0	4000	0.1735	0.7976	0.8853	0.8392	0.9470
0.038	5.0	5000	0.1916	0.8057	0.8640	0.8338	0.9455

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1