--- base_model: meta-llama/Llama-3.2-1B library_name: peft license: llama3.2 metrics: - accuracy tags: - generated_from_trainer model-index: - name: llama3.2-finetuned-newsclassify results: [] --- # 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: 0.0941 - Balanced Accuracy: 0.984 - Accuracy: 0.984 - F1-score: 0.9839 - Classification-report: precision recall f1-score support 0 1.00 0.92 0.96 50 1 1.00 1.00 1.00 50 2 0.98 1.00 0.99 50 3 1.00 1.00 1.00 50 4 0.94 1.00 0.97 50 accuracy 0.98 250 macro avg 0.98 0.98 0.98 250 weighted avg 0.98 0.98 0.98 250 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Balanced Accuracy | Accuracy | F1-score | Classification-report | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|:--------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.001 | 1.0 | 157 | 0.0941 | 0.984 | 0.984 | 0.9839 | precision recall f1-score support 0 1.00 0.92 0.96 50 1 1.00 1.00 1.00 50 2 0.98 1.00 0.99 50 3 1.00 1.00 1.00 50 4 0.94 1.00 0.97 50 accuracy 0.98 250 macro avg 0.98 0.98 0.98 250 weighted avg 0.98 0.98 0.98 250 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.1+cu121 - Datasets 3.0.2 - Tokenizers 0.20.1