llama3.2-finetuned-newsclassify

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0205

• Balanced Accuracy: 0.992

• Accuracy: 0.992

• F1-score: 0.9920

• Classification-report: precision recall f1-score support

       0       1.00      0.96      0.98        50
       1       1.00      1.00      1.00        50
       2       1.00      1.00      1.00        50
       3       1.00      1.00      1.00        50
       4       0.96      1.00      0.98        50
  

  accuracy 0.99 250 macro avg 0.99 0.99 0.99 250

weighted avg 0.99 0.99 0.99 250

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use 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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Balanced Accuracy	Accuracy	F1-score	Classification-report
0.0	1.0	157	0.0405	0.9880	0.988	0.9880	precision recall f1-score support

       0       1.00      0.94      0.97        50
       1       1.00      1.00      1.00        50
       2       1.00      1.00      1.00        50
       3       1.00      1.00      1.00        50
       4       0.94      1.00      0.97        50

accuracy                           0.99       250

macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 | | 0.0 | 2.0 | 314 | 0.0300 | 0.9880 | 0.988 | 0.9880 | precision recall f1-score support

       0       1.00      0.94      0.97        50
       1       1.00      1.00      1.00        50
       2       1.00      1.00      1.00        50
       3       1.00      1.00      1.00        50
       4       0.94      1.00      0.97        50

accuracy                           0.99       250

macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 | | 0.0 | 3.0 | 471 | 0.0177 | 0.992 | 0.992 | 0.9920 | precision recall f1-score support

       0       1.00      0.96      0.98        50
       1       1.00      1.00      1.00        50
       2       1.00      1.00      1.00        50
       3       1.00      1.00      1.00        50
       4       0.96      1.00      0.98        50

accuracy                           0.99       250

macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 | | 0.0 | 4.0 | 628 | 0.0205 | 0.992 | 0.992 | 0.9920 | precision recall f1-score support

       0       1.00      0.96      0.98        50
       1       1.00      1.00      1.00        50
       2       1.00      1.00      1.00        50
       3       1.00      1.00      1.00        50
       4       0.96      1.00      0.98        50

accuracy                           0.99       250

macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 |

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.4.1+cu121
• Datasets 3.0.2
• Tokenizers 0.20.1