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--- |
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library_name: transformers |
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base_model: peiyi9979/math-shepherd-mistral-7b-prm |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: v1_5_mistral_full_1122 |
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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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# v1_5_mistral_full_1122 |
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This model is a fine-tuned version of [peiyi9979/math-shepherd-mistral-7b-prm](https://huggingface.co/peiyi9979/math-shepherd-mistral-7b-prm) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2520 |
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- Accuracy: 0.9035 |
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- Precision: 0.8317 |
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- Recall: 0.7925 |
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- F1: 0.8116 |
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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: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 765837 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 8 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.3603 | 0.0575 | 20 | 0.4322 | 0.7550 | 0.5189 | 0.9057 | 0.6598 | |
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| 0.3252 | 0.1151 | 40 | 0.3813 | 0.8663 | 0.825 | 0.6226 | 0.7097 | |
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| 0.3574 | 0.1726 | 60 | 0.3824 | 0.8045 | 0.5789 | 0.9340 | 0.7148 | |
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| 0.31 | 0.2301 | 80 | 0.3275 | 0.8614 | 0.7155 | 0.7830 | 0.7477 | |
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| 0.2803 | 0.2877 | 100 | 0.3611 | 0.8738 | 0.7723 | 0.7358 | 0.7536 | |
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| 0.3893 | 0.3452 | 120 | 0.3245 | 0.8416 | 0.6694 | 0.7830 | 0.7217 | |
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| 0.3624 | 0.4027 | 140 | 0.3172 | 0.8812 | 0.8372 | 0.6792 | 0.75 | |
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| 0.3081 | 0.4603 | 160 | 0.3283 | 0.8639 | 0.7742 | 0.6792 | 0.7236 | |
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| 0.242 | 0.5178 | 180 | 0.2907 | 0.8837 | 0.7658 | 0.8019 | 0.7834 | |
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| 0.2692 | 0.5753 | 200 | 0.2787 | 0.8911 | 0.7925 | 0.7925 | 0.7925 | |
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| 0.2866 | 0.6329 | 220 | 0.2675 | 0.8787 | 0.7478 | 0.8113 | 0.7783 | |
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| 0.27 | 0.6904 | 240 | 0.2702 | 0.9035 | 0.8317 | 0.7925 | 0.8116 | |
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| 0.3112 | 0.7479 | 260 | 0.2605 | 0.9059 | 0.8864 | 0.7358 | 0.8041 | |
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| 0.2032 | 0.8055 | 280 | 0.2700 | 0.9010 | 0.8587 | 0.7453 | 0.7980 | |
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| 0.2326 | 0.8630 | 300 | 0.2549 | 0.9059 | 0.8333 | 0.8019 | 0.8173 | |
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| 0.2714 | 0.9205 | 320 | 0.2511 | 0.9035 | 0.8317 | 0.7925 | 0.8116 | |
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| 0.2562 | 0.9781 | 340 | 0.2520 | 0.9035 | 0.8317 | 0.7925 | 0.8116 | |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.4.0+cu118 |
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- Datasets 3.0.0 |
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- Tokenizers 0.20.1 |
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