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

Loss: 0.6052
Accuracy: 0.6467
F1: 0.5656
Precision: 0.5306
Recall: 0.6467

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: 3e-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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	101	0.6449	0.6017	0.5169	0.4906	0.6017
No log	2.0	202	0.6052	0.6467	0.5656	0.5306	0.6467
No log	3.0	303	0.6169	0.6255	0.5427	0.5043	0.6255
No log	4.0	404	0.6344	0.6342	0.5513	0.5153	0.6342
0.5711	5.0	505	0.6630	0.6317	0.5489	0.5106	0.6317

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

bellge/model_upgrade

model_upgrade

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bellge/
model_upgrade