results – AI Model by Gkumi | AlphaNeural AI

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token-classification

generated_from_trainer

distilbert/distilbert-base-cased

autotrain_compatible

endpoints_compatible

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results

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0452
Precision: 0.8809
Recall: 0.9161
F1: 0.8982
Accuracy: 0.9860

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: 0.0005
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1732	1.0	2180	0.1202	0.5106	0.6557	0.5741	0.9612
0.1057	2.0	4360	0.0821	0.6500	0.7866	0.7118	0.9728
0.0639	3.0	6540	0.0573	0.7953	0.8256	0.8102	0.9812
0.0347	4.0	8720	0.0482	0.8531	0.9030	0.8773	0.9846
0.0212	5.0	10900	0.0452	0.8809	0.9161	0.8982	0.9860

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1