--- base_model: meta-llama/Llama-3.2-1B library_name: peft license: llama3.2 metrics: - accuracy - f1 - recall - precision tags: - generated_from_trainer model-index: - name: llama3.2-finetuned-newsclassify results: [] language: - en pipeline_tag: text-classification --- # llama3.2-finetuned-newsclassify This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/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