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CS221-roberta-base-finetuned-semeval-NT

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roberta
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  1. README.md +15 -20
  2. model.safetensors +1 -1
README.md CHANGED
@@ -19,10 +19,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6633
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- - F1: 0.7703
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- - Roc Auc: 0.8283
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- - Accuracy: 0.4657
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  ## Model description
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@@ -54,22 +54,17 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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- | 0.4375 | 1.0 | 277 | 0.4129 | 0.7176 | 0.7882 | 0.3827 |
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- | 0.332 | 2.0 | 554 | 0.3644 | 0.7443 | 0.8070 | 0.4404 |
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- | 0.2598 | 3.0 | 831 | 0.3595 | 0.7527 | 0.8130 | 0.4440 |
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- | 0.1818 | 4.0 | 1108 | 0.4010 | 0.7536 | 0.8139 | 0.4531 |
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- | 0.1235 | 5.0 | 1385 | 0.4321 | 0.7382 | 0.8020 | 0.4260 |
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- | 0.1116 | 6.0 | 1662 | 0.4640 | 0.7565 | 0.8170 | 0.4621 |
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- | 0.064 | 7.0 | 1939 | 0.5042 | 0.7543 | 0.8138 | 0.4549 |
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- | 0.0517 | 8.0 | 2216 | 0.5384 | 0.7562 | 0.8149 | 0.4639 |
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- | 0.0172 | 9.0 | 2493 | 0.5473 | 0.7617 | 0.8208 | 0.4621 |
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- | 0.0114 | 10.0 | 2770 | 0.5879 | 0.7691 | 0.8266 | 0.4747 |
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- | 0.0179 | 11.0 | 3047 | 0.6149 | 0.7635 | 0.8213 | 0.4639 |
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- | 0.0054 | 12.0 | 3324 | 0.6756 | 0.7642 | 0.8245 | 0.4567 |
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- | 0.0038 | 13.0 | 3601 | 0.6633 | 0.7703 | 0.8283 | 0.4657 |
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- | 0.003 | 14.0 | 3878 | 0.6770 | 0.7662 | 0.8235 | 0.4639 |
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- | 0.0025 | 15.0 | 4155 | 0.6990 | 0.7661 | 0.8244 | 0.4693 |
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- | 0.003 | 16.0 | 4432 | 0.6926 | 0.7680 | 0.8248 | 0.4657 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5269
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+ - F1: 0.7240
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+ - Roc Auc: 0.7938
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+ - Accuracy: 0.4639
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.4269 | 1.0 | 277 | 0.3920 | 0.6532 | 0.7374 | 0.4025 |
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+ | 0.3157 | 2.0 | 554 | 0.3683 | 0.6965 | 0.7692 | 0.4152 |
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+ | 0.2287 | 3.0 | 831 | 0.3818 | 0.6849 | 0.7667 | 0.4314 |
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+ | 0.1779 | 4.0 | 1108 | 0.4116 | 0.6927 | 0.7689 | 0.4097 |
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+ | 0.1274 | 5.0 | 1385 | 0.4471 | 0.6991 | 0.7729 | 0.4314 |
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+ | 0.1036 | 6.0 | 1662 | 0.4658 | 0.7166 | 0.7848 | 0.4549 |
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+ | 0.0684 | 7.0 | 1939 | 0.5065 | 0.7133 | 0.7840 | 0.4422 |
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+ | 0.055 | 8.0 | 2216 | 0.5269 | 0.7240 | 0.7938 | 0.4639 |
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+ | 0.0156 | 9.0 | 2493 | 0.5896 | 0.7157 | 0.7920 | 0.4513 |
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+ | 0.0173 | 10.0 | 2770 | 0.6118 | 0.7215 | 0.7868 | 0.4477 |
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+ | 0.0171 | 11.0 | 3047 | 0.6322 | 0.7234 | 0.7941 | 0.4513 |
 
 
 
 
 
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  ### Framework versions
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