distilbert-base-uncased-finetuned-source-recognition – AI Model by SyaeMiko | AlphaNeural AI

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SyaeMiko/
distilbert-base-uncased-finetuned-source-recognition

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-source-recognition

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

Loss: 0.1588
Accuracy: 0.9550
Precision: 0.9550
Recall: 0.9550
F1: 0.9550

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: 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	Accuracy	Precision	Recall	F1
0.132	1.0	3009	0.1251	0.9568	0.9568	0.9568	0.9568
0.1066	2.0	6018	0.1184	0.9591	0.9592	0.9591	0.9591
0.078	3.0	9027	0.1432	0.9598	0.9598	0.9598	0.9598
0.0527	4.0	12036	0.1773	0.9589	0.9591	0.9589	0.9589
0.0313	5.0	15045	0.2088	0.9591	0.9591	0.9591	0.9591

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0