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--- |
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base_model: meta-llama/Llama-3.2-1B |
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library_name: peft |
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license: llama3.2 |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: llama3.2-finetuned-newsclassify |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# llama3.2-finetuned-newsclassify |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0205 |
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- Balanced Accuracy: 0.992 |
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- Accuracy: 0.992 |
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- F1-score: 0.9920 |
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- Classification-report: precision recall f1-score support |
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0 1.00 0.96 0.98 50 |
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1 1.00 1.00 1.00 50 |
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2 1.00 1.00 1.00 50 |
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3 1.00 1.00 1.00 50 |
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4 0.96 1.00 0.98 50 |
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accuracy 0.99 250 |
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macro avg 0.99 0.99 0.99 250 |
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weighted avg 0.99 0.99 0.99 250 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Balanced Accuracy | Accuracy | F1-score | Classification-report | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------:|:--------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.0 | 1.0 | 157 | 0.0405 | 0.9880 | 0.988 | 0.9880 | precision recall f1-score support |
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0 1.00 0.94 0.97 50 |
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1 1.00 1.00 1.00 50 |
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2 1.00 1.00 1.00 50 |
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3 1.00 1.00 1.00 50 |
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4 0.94 1.00 0.97 50 |
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accuracy 0.99 250 |
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macro avg 0.99 0.99 0.99 250 |
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weighted avg 0.99 0.99 0.99 250 |
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| 0.0 | 2.0 | 314 | 0.0300 | 0.9880 | 0.988 | 0.9880 | precision recall f1-score support |
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0 1.00 0.94 0.97 50 |
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1 1.00 1.00 1.00 50 |
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2 1.00 1.00 1.00 50 |
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3 1.00 1.00 1.00 50 |
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4 0.94 1.00 0.97 50 |
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accuracy 0.99 250 |
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macro avg 0.99 0.99 0.99 250 |
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weighted avg 0.99 0.99 0.99 250 |
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| 0.0 | 3.0 | 471 | 0.0177 | 0.992 | 0.992 | 0.9920 | precision recall f1-score support |
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0 1.00 0.96 0.98 50 |
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1 1.00 1.00 1.00 50 |
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2 1.00 1.00 1.00 50 |
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3 1.00 1.00 1.00 50 |
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4 0.96 1.00 0.98 50 |
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accuracy 0.99 250 |
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macro avg 0.99 0.99 0.99 250 |
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weighted avg 0.99 0.99 0.99 250 |
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| 0.0 | 4.0 | 628 | 0.0205 | 0.992 | 0.992 | 0.9920 | precision recall f1-score support |
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0 1.00 0.96 0.98 50 |
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1 1.00 1.00 1.00 50 |
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2 1.00 1.00 1.00 50 |
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3 1.00 1.00 1.00 50 |
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4 0.96 1.00 0.98 50 |
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accuracy 0.99 250 |
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macro avg 0.99 0.99 0.99 250 |
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weighted avg 0.99 0.99 0.99 250 |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.46.0 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.20.1 |
