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

Loss: 0.0152
Accuracy: 0.9957
F1: 0.9957

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: 1e-05
train_batch_size: 82
eval_batch_size: 82
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.0786	1.0	596	0.0476	0.9846	0.9846
0.0217	2.0	1192	0.0265	0.9912	0.9912
0.0224	3.0	1788	0.0171	0.9953	0.9952
0.0072	4.0	2384	0.0158	0.9953	0.9952
0.0157	5.0	2980	0.0152	0.9957	0.9957

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

hanyuany14/finetuned-DSCS24-final-project-distilbert-base-uncased-text-classification

finetuned-DSCS24-final-project-distilbert-base-uncased-text-classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hanyuany14/
finetuned-DSCS24-final-project-distilbert-base-uncased-text-classification