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--- |
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license: mit |
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base_model: roberta-base |
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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: roberta_multiple_choice |
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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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# roberta_multiple_choice |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1836 |
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- Accuracy: 0.955 |
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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: 5e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 100 |
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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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| 1.4783 | 1.0 | 1725 | 1.3350 | 0.405 | |
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| 1.3252 | 2.0 | 3450 | 1.1893 | 0.565 | |
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| 1.2436 | 3.0 | 5175 | 1.0734 | 0.57 | |
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| 1.1636 | 4.0 | 6900 | 0.9469 | 0.61 | |
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| 1.0818 | 5.0 | 8625 | 0.8554 | 0.675 | |
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| 0.9999 | 6.0 | 10350 | 0.7437 | 0.715 | |
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| 0.9227 | 7.0 | 12075 | 0.6424 | 0.76 | |
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| 0.8361 | 8.0 | 13800 | 0.5657 | 0.8 | |
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| 0.7636 | 9.0 | 15525 | 0.5241 | 0.815 | |
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| 0.696 | 10.0 | 17250 | 0.4742 | 0.83 | |
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| 0.6343 | 11.0 | 18975 | 0.4325 | 0.855 | |
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| 0.5758 | 12.0 | 20700 | 0.4263 | 0.86 | |
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| 0.5377 | 13.0 | 22425 | 0.3876 | 0.835 | |
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| 0.4889 | 14.0 | 24150 | 0.3675 | 0.87 | |
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| 0.4467 | 15.0 | 25875 | 0.3221 | 0.885 | |
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| 0.4172 | 16.0 | 27600 | 0.3175 | 0.89 | |
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| 0.3899 | 17.0 | 29325 | 0.3048 | 0.9 | |
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| 0.3649 | 18.0 | 31050 | 0.2795 | 0.91 | |
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| 0.3413 | 19.0 | 32775 | 0.2669 | 0.905 | |
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| 0.3219 | 20.0 | 34500 | 0.2764 | 0.92 | |
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| 0.3055 | 21.0 | 36225 | 0.2478 | 0.93 | |
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| 0.2826 | 22.0 | 37950 | 0.2069 | 0.94 | |
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| 0.2733 | 23.0 | 39675 | 0.2230 | 0.945 | |
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| 0.2537 | 24.0 | 41400 | 0.2138 | 0.945 | |
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| 0.2412 | 25.0 | 43125 | 0.1887 | 0.95 | |
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| 0.2281 | 26.0 | 44850 | 0.1923 | 0.955 | |
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| 0.2188 | 27.0 | 46575 | 0.1663 | 0.96 | |
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| 0.2031 | 28.0 | 48300 | 0.2389 | 0.945 | |
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| 0.2008 | 29.0 | 50025 | 0.1823 | 0.955 | |
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| 0.1858 | 30.0 | 51750 | 0.1994 | 0.95 | |
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| 0.1854 | 31.0 | 53475 | 0.2013 | 0.96 | |
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| 0.1721 | 32.0 | 55200 | 0.1836 | 0.955 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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