checkpoints – AI Model by Kl1en3ver | AlphaNeural AI

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Kl1en3ver/
checkpoints

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

endpoints_compatible

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checkpoints

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: 1.3402
Accuracy: 0.8646
F1: 0.8427
Precision: 0.8436
Recall: 0.8445

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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.4083	1.0	570	1.4497	0.7934	0.7845	0.7743	0.8101
1.3497	2.0	1140	1.3323	0.8307	0.8161	0.8101	0.8312
1.2217	3.0	1710	1.3071	0.8544	0.8341	0.8334	0.8380
1.1486	4.0	2280	1.2961	0.8539	0.8389	0.8394	0.8430
1.0651	5.0	2850	1.2995	0.8618	0.8428	0.8417	0.8470

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2