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: 0.1529
Precision: 0.4993
Recall: 0.5397
F1: 0.5187
Accuracy: 0.9661

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: 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	205	0.1641	0.3048	0.3556	0.3282	0.9556
No log	2.0	410	0.1387	0.4741	0.4365	0.4545	0.9642
0.1943	3.0	615	0.1430	0.4690	0.4810	0.4749	0.9648
0.1943	4.0	820	0.1481	0.4993	0.5365	0.5172	0.9655
0.0496	5.0	1025	0.1529	0.4993	0.5397	0.5187	0.9661

Framework versions

Transformers 4.38.2
Pytorch 2.2.0+cpu
Datasets 2.18.0
Tokenizers 0.15.2

cria111/training_with_callbacks

training_with_callbacks

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

cria111/
training_with_callbacks