distilbert-base-uncased-lora-feedback-classification – AI Model by Philseok | AlphaNeural AI

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Philseok/
distilbert-base-uncased-lora-feedback-classification

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distilbert-base-uncased-lora-feedback-classification

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

Loss: 0.3213
Accuracy: 0.8757
Precision: 0.8869
Recall: 0.9159
F1: 0.9011
Roc Auc: 0.9403

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: 0.0001
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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Roc Auc
0.2968	1.0	51	0.3062	0.8815	0.8950	0.9159	0.9053	0.9417
0.3910	2.0	102	0.3117	0.8815	0.9023	0.9065	0.9044	0.9397
0.3319	3.0	153	0.3197	0.8728	0.8864	0.9112	0.8986	0.9409
0.2811	4.0	204	0.3194	0.8757	0.8904	0.9112	0.9007	0.9406
0.2593	5.0	255	0.3213	0.8757	0.8869	0.9159	0.9011	0.9403

Framework versions

PEFT 0.18.1
Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.6.1
Tokenizers 0.22.2