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nickhobbs09/
checkpoints

Name: checkpoints
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generated_from_trainer

distilbert/distilbert-base-uncased

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checkpoints

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: 0.3546
Precision: 0.4974
Recall: 0.6016
F1: 0.5446
Accuracy: 0.8802

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: 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3168	1.0	48	0.5115	0.2667	0.3976	0.3193	0.8048
0.1789	2.0	96	0.4051	0.3864	0.4909	0.4324	0.8504
0.2047	3.0	144	0.3673	0.4541	0.5641	0.5032	0.8676
0.3418	4.0	192	0.3546	0.4974	0.6016	0.5446	0.8802
0.1801	5.0	240	0.3867	0.5314	0.6081	0.5671	0.8795
0.1971	6.0	288	0.4095	0.5590	0.6272	0.5911	0.8820
0.1001	7.0	336	0.4249	0.5467	0.6169	0.5797	0.8815
0.0915	8.0	384	0.4487	0.5587	0.6202	0.5879	0.8805
0.1072	9.0	432	0.4611	0.5637	0.6225	0.5916	0.8804
0.0729	10.0	480	0.4469	0.5720	0.6334	0.6011	0.8846
0.0532	11.0	528	0.4702	0.5756	0.6370	0.6047	0.8827
0.0665	12.0	576	0.4744	0.5785	0.6430	0.6090	0.8842
0.0932	13.0	624	0.4899	0.5774	0.6421	0.6081	0.8830
0.043	14.0	672	0.4911	0.5845	0.6474	0.6143	0.8837
0.0662	15.0	720	0.4866	0.5830	0.6483	0.6139	0.8839

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

nickhobbs09/checkpoints

checkpoints

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nickhobbs09/
checkpoints