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bert_multiple_choice

Multiple Choice
Transformers
PyTorch
bert
Generated from Trainer
Model card Files
xet
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update model card README.md

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@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3676
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- - Accuracy: 0.45
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  ## Model description
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@@ -49,36 +49,38 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 12 | 1.6011 | 0.2 |
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- | No log | 2.0 | 24 | 1.6026 | 0.2 |
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- | No log | 3.0 | 36 | 1.5957 | 0.3 |
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- | No log | 4.0 | 48 | 1.5885 | 0.35 |
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- | No log | 5.0 | 60 | 1.5782 | 0.4 |
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- | No log | 6.0 | 72 | 1.5609 | 0.45 |
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- | No log | 7.0 | 84 | 1.5448 | 0.45 |
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- | No log | 8.0 | 96 | 1.5432 | 0.45 |
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- | No log | 9.0 | 108 | 1.5166 | 0.5 |
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- | No log | 10.0 | 120 | 1.5045 | 0.5 |
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- | No log | 11.0 | 132 | 1.5056 | 0.5 |
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- | No log | 12.0 | 144 | 1.5011 | 0.5 |
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- | No log | 13.0 | 156 | 1.5010 | 0.4 |
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- | No log | 14.0 | 168 | 1.4879 | 0.4 |
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- | No log | 15.0 | 180 | 1.4721 | 0.5 |
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- | No log | 16.0 | 192 | 1.4582 | 0.45 |
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- | No log | 17.0 | 204 | 1.4659 | 0.5 |
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- | No log | 18.0 | 216 | 1.4445 | 0.4 |
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- | No log | 19.0 | 228 | 1.4404 | 0.45 |
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- | No log | 20.0 | 240 | 1.4322 | 0.45 |
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- | No log | 21.0 | 252 | 1.4284 | 0.4 |
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- | No log | 22.0 | 264 | 1.4045 | 0.45 |
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- | No log | 23.0 | 276 | 1.3910 | 0.5 |
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- | No log | 24.0 | 288 | 1.4120 | 0.45 |
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- | No log | 25.0 | 300 | 1.3679 | 0.5 |
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- | No log | 26.0 | 312 | 1.3662 | 0.5 |
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- | No log | 27.0 | 324 | 1.3865 | 0.55 |
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- | No log | 28.0 | 336 | 1.4005 | 0.5 |
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- | No log | 29.0 | 348 | 1.3866 | 0.45 |
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- | No log | 30.0 | 360 | 1.3676 | 0.45 |
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4718
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+ - Accuracy: 0.4
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.6321 | 1.0 | 12 | 1.5869 | 0.45 |
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+ | 1.6206 | 2.0 | 24 | 1.5864 | 0.45 |
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+ | 1.6088 | 3.0 | 36 | 1.5849 | 0.45 |
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+ | 1.6098 | 4.0 | 48 | 1.5760 | 0.5 |
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+ | 1.5938 | 5.0 | 60 | 1.5746 | 0.55 |
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+ | 1.5811 | 6.0 | 72 | 1.5726 | 0.5 |
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+ | 1.5968 | 7.0 | 84 | 1.5699 | 0.55 |
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+ | 1.5521 | 8.0 | 96 | 1.5627 | 0.5 |
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+ | 1.5438 | 9.0 | 108 | 1.5485 | 0.45 |
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+ | 1.5605 | 10.0 | 120 | 1.5456 | 0.35 |
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+ | 1.552 | 11.0 | 132 | 1.5448 | 0.35 |
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+ | 1.5065 | 12.0 | 144 | 1.5466 | 0.5 |
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+ | 1.5075 | 13.0 | 156 | 1.5490 | 0.45 |
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+ | 1.4757 | 14.0 | 168 | 1.5412 | 0.4 |
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+ | 1.4504 | 15.0 | 180 | 1.5292 | 0.45 |
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+ | 1.3916 | 16.0 | 192 | 1.5161 | 0.3 |
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+ | 1.4132 | 17.0 | 204 | 1.5146 | 0.35 |
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+ | 1.3652 | 18.0 | 216 | 1.4985 | 0.35 |
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+ | 1.3303 | 19.0 | 228 | 1.4864 | 0.35 |
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+ | 1.3441 | 20.0 | 240 | 1.4845 | 0.4 |
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+ | 1.3161 | 21.0 | 252 | 1.4953 | 0.35 |
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+ | 1.3025 | 22.0 | 264 | 1.4689 | 0.4 |
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+ | 1.2371 | 23.0 | 276 | 1.4582 | 0.4 |
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+ | 1.2468 | 24.0 | 288 | 1.4523 | 0.4 |
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+ | 1.2156 | 25.0 | 300 | 1.4673 | 0.45 |
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+ | 1.1791 | 26.0 | 312 | 1.4608 | 0.4 |
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+ | 1.1919 | 27.0 | 324 | 1.4125 | 0.4 |
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+ | 1.1926 | 28.0 | 336 | 1.4655 | 0.45 |
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+ | 1.1473 | 29.0 | 348 | 1.4629 | 0.4 |
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+ | 1.1132 | 30.0 | 360 | 1.4481 | 0.4 |
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+ | 1.1064 | 31.0 | 372 | 1.4754 | 0.4 |
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+ | 1.1352 | 32.0 | 384 | 1.4718 | 0.4 |
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  ### Framework versions