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--- |
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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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model-index: |
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- name: fresh-2-layer-medmcqa10000-distill-of-fresh-2-layer-gpqa_EVAL_gpqa |
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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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# fresh-2-layer-medmcqa10000-distill-of-fresh-2-layer-gpqa_EVAL_gpqa |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 7.2371 |
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- Accuracy: 0.7222 |
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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: 0.0005 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 321 |
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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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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.32 | 100 | 13.8640 | 0.4495 | |
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| No log | 0.64 | 200 | 11.8895 | 0.5152 | |
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| No log | 0.96 | 300 | 10.3828 | 0.5303 | |
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| No log | 1.28 | 400 | 9.0300 | 0.5657 | |
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| 3.3496 | 1.6 | 500 | 8.6264 | 0.5808 | |
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| 3.3496 | 1.92 | 600 | 8.8207 | 0.6061 | |
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| 3.3496 | 2.24 | 700 | 8.8302 | 0.5960 | |
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| 3.3496 | 2.56 | 800 | 8.8428 | 0.6566 | |
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| 3.3496 | 2.88 | 900 | 7.9552 | 0.6364 | |
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| 0.8968 | 3.19 | 1000 | 8.4617 | 0.6263 | |
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| 0.8968 | 3.51 | 1100 | 8.8555 | 0.6616 | |
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| 0.8968 | 3.83 | 1200 | 7.5445 | 0.6566 | |
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| 0.8968 | 4.15 | 1300 | 7.6791 | 0.6717 | |
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| 0.8968 | 4.47 | 1400 | 7.8363 | 0.6616 | |
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| 0.4853 | 4.79 | 1500 | 7.6269 | 0.6515 | |
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| 0.4853 | 5.11 | 1600 | 7.5024 | 0.6919 | |
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| 0.4853 | 5.43 | 1700 | 7.4191 | 0.6717 | |
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| 0.4853 | 5.75 | 1800 | 7.6877 | 0.6768 | |
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| 0.4853 | 6.07 | 1900 | 7.4651 | 0.6818 | |
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| 0.3197 | 6.39 | 2000 | 7.4452 | 0.6970 | |
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| 0.3197 | 6.71 | 2100 | 7.2401 | 0.7121 | |
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| 0.3197 | 7.03 | 2200 | 7.4038 | 0.7121 | |
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| 0.3197 | 7.35 | 2300 | 7.1982 | 0.7071 | |
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| 0.3197 | 7.67 | 2400 | 7.2287 | 0.7071 | |
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| 0.2394 | 7.99 | 2500 | 7.2371 | 0.7222 | |
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| 0.2394 | 8.31 | 2600 | 7.2513 | 0.7071 | |
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| 0.2394 | 8.63 | 2700 | 7.3788 | 0.6919 | |
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| 0.2394 | 8.95 | 2800 | 7.1303 | 0.7071 | |
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| 0.2394 | 9.27 | 2900 | 7.1608 | 0.7121 | |
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| 0.1744 | 9.58 | 3000 | 7.1039 | 0.7222 | |
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### Framework versions |
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- Transformers 4.34.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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