distilbert-classifier

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: 0.0023
Accuracy: 1.0

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: 0.001
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3733	1.0	10	0.0722	1.0
0.0346	2.0	20	0.0150	1.0
0.0079	3.0	30	0.0084	1.0
0.0031	4.0	40	0.0089	1.0
0.0030	5.0	50	0.0054	1.0
0.0013	6.0	60	0.0053	1.0
0.0016	7.0	70	0.0034	1.0
0.0011	8.0	80	0.0031	1.0
0.0010	9.0	90	0.0030	1.0
0.0007	10.0	100	0.0029	1.0
0.0008	11.0	110	0.0026	1.0
0.0004	12.0	120	0.0025	1.0
0.0007	13.0	130	0.0024	1.0
0.0009	14.0	140	0.0024	1.0
0.0003	15.0	150	0.0023	1.0

Framework versions

Transformers 5.19.0
Pytorch 2.14.1
Datasets 5.1.0
Tokenizers 0.23.2

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franckzhuang/distilbert-classifier

distilbert-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

distilbert-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions