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--- |
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base_model: meta-llama/Llama-2-13b-hf |
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library_name: peft |
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license: llama2 |
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metrics: |
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- accuracy |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: llama2_13B_LORA_FOR_CLASSIFICATION |
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results: [] |
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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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# llama2_13B_LORA_FOR_CLASSIFICATION |
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This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5708 |
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- Balanced Accuracy: 0.7079 |
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- Accuracy: 0.7530 |
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- Micro F1: 0.7530 |
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- Macro F1: 0.6771 |
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- Weighted F1: 0.7669 |
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- Classification Report: precision recall f1-score support |
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0 0.89 0.79 0.83 857 |
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1 0.44 0.63 0.52 232 |
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accuracy 0.75 1089 |
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macro avg 0.67 0.71 0.68 1089 |
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weighted avg 0.79 0.75 0.77 1089 |
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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: 24 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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 | Accuracy | Balanced Accuracy | Classification Report | Validation Loss | Macro F1 | Micro F1 | Weighted F1 | |
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|:-------------:|:-----:|:----:|:--------:|:-----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:--------:|:--------:|:-----------:| |
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| 0.4853 | 2.0 | 522 | 0.7750 | 0.7297 | precision recall f1-score support |
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0 0.90 0.81 0.85 857 |
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1 0.48 0.65 0.55 232 |
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accuracy 0.78 1089 |
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macro avg 0.69 0.73 0.70 1089 |
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weighted avg 0.81 0.78 0.79 1089 |
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| 0.5482 | 0.7009 | 0.7750 | 0.7864 | |
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| 0.4116 | 3.0 | 783 | 0.7668 | 0.7182 | precision recall f1-score support |
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0 0.89 0.80 0.84 857 |
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1 0.47 0.63 0.54 232 |
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accuracy 0.77 1089 |
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macro avg 0.68 0.72 0.69 1089 |
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weighted avg 0.80 0.77 0.78 1089 |
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| 0.5497 | 0.6903 | 0.7668 | 0.7786 | |
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| 0.3224 | 4.0 | 1044 | 0.5708 | 0.7079 | 0.7530 | 0.7530 | 0.6771 | 0.7669 | precision recall f1-score support |
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0 0.89 0.79 0.83 857 |
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1 0.44 0.63 0.52 232 |
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accuracy 0.75 1089 |
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macro avg 0.67 0.71 0.68 1089 |
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weighted avg 0.79 0.75 0.77 1089 |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu118 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
