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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: mistralai/Mistral-7B-v0.1 |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: Mistral_7B_MT |
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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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# Mistral_7B_MT |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8388 |
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- Accuracy: 0.8167 |
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- Precision: 0.8519 |
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- Recall: 0.7667 |
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- F1 score: 0.8070 |
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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: 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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 score | Precision | Recall | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:--------:|:---------:|:------:|:---------------:| |
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| 1.687 | 0.25 | 200 | 0.6233 | 0.4378 | 0.8627 | 0.2933 | 2.0030 | |
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| 0.9482 | 0.5 | 400 | 0.68 | 0.5616 | 0.8913 | 0.41 | 1.4557 | |
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| 0.9232 | 0.75 | 600 | 0.72 | 0.6471 | 0.875 | 0.5133 | 0.8805 | |
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| 0.7781 | 1.0 | 800 | 0.57 | 0.3246 | 0.7561 | 0.2067 | 1.4515 | |
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| 0.5468 | 1.25 | 1000 | 0.7233 | 0.6483 | 0.8895 | 0.51 | 0.8474 | |
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| 0.5549 | 1.5 | 1200 | 0.7767 | 0.7403 | 0.8843 | 0.6367 | 0.7168 | |
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| 0.4883 | 1.75 | 1400 | 0.8 | 0.7719 | 0.8982 | 0.6767 | 0.6943 | |
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| 0.4639 | 2.0 | 1600 | 0.7767 | 0.7276 | 0.9323 | 0.5967 | 0.7637 | |
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| 0.3804 | 2.25 | 1800 | 0.7617 | 0.7146 | 0.8905 | 0.5967 | 0.8467 | |
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| 0.3847 | 2.5 | 2000 | 0.81 | 0.7942 | 0.8661 | 0.7333 | 0.6699 | |
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| 0.346 | 2.75 | 2200 | 0.7833 | 0.7575 | 0.8602 | 0.6767 | 0.8569 | |
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| 0.3488 | 3.0 | 2400 | 0.7824 | 0.815 | 0.9238 | 0.6867 | 0.7878 | |
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| 0.2654 | 3.25 | 2600 | 1.0799 | 0.7683 | 0.9259 | 0.5833 | 0.7157 | |
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| 0.2506 | 3.5 | 2800 | 0.8567 | 0.8033 | 0.9062 | 0.6767 | 0.7748 | |
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| 0.2574 | 3.75 | 3000 | 0.7490 | 0.8083 | 0.7846 | 0.85 | 0.816 | |
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| 0.2137 | 4.0 | 3200 | 0.7665 | 0.8333 | 0.8546 | 0.8033 | 0.8282 | |
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| 0.1335 | 4.25 | 3400 | 0.8591 | 0.8133 | 0.8013 | 0.8333 | 0.8170 | |
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| 0.1486 | 4.5 | 3600 | 0.9781 | 0.83 | 0.9091 | 0.7333 | 0.8118 | |
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| 0.126 | 4.75 | 3800 | 0.8723 | 0.8217 | 0.8642 | 0.7633 | 0.8106 | |
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| 0.1474 | 5.0 | 4000 | 0.8388 | 0.8167 | 0.8519 | 0.7667 | 0.8070 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.44.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
