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nguyenkhoa2407
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favs_filter_classification_v2

Text Classification
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PyTorch
bert
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Eval Results
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@@ -22,10 +22,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.9666666666666667
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  - name: Accuracy
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  type: accuracy
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- value: 0.9375
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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
@@ -35,10 +35,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_v2 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2721
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- - F1: 0.9667
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- - Roc Auc: 0.9772
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- - Accuracy: 0.9375
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  ## Model description
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@@ -69,26 +69,26 @@ 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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- | 0.7591 | 1.0 | 14 | 0.6660 | 0.3137 | 0.5541 | 0.0 |
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- | 0.6506 | 2.0 | 28 | 0.5575 | 0.4706 | 0.6451 | 0.0625 |
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- | 0.5006 | 3.0 | 42 | 0.5010 | 0.5385 | 0.6846 | 0.0625 |
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- | 0.4416 | 4.0 | 56 | 0.4536 | 0.6538 | 0.7528 | 0.125 |
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- | 0.3815 | 5.0 | 70 | 0.4127 | 0.8070 | 0.8589 | 0.5 |
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- | 0.3468 | 6.0 | 84 | 0.3748 | 0.8621 | 0.8984 | 0.5625 |
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- | 0.3316 | 7.0 | 98 | 0.3487 | 0.8621 | 0.8984 | 0.5625 |
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- | 0.2834 | 8.0 | 112 | 0.3191 | 0.9 | 0.9317 | 0.6875 |
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- | 0.2565 | 9.0 | 126 | 0.2970 | 0.9492 | 0.9606 | 0.875 |
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- | 0.2241 | 10.0 | 140 | 0.2721 | 0.9667 | 0.9772 | 0.9375 |
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- | 0.214 | 11.0 | 154 | 0.2563 | 0.9492 | 0.9606 | 0.875 |
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- | 0.2041 | 12.0 | 168 | 0.2499 | 0.9492 | 0.9606 | 0.875 |
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- | 0.1831 | 13.0 | 182 | 0.2353 | 0.9492 | 0.9606 | 0.875 |
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- | 0.1852 | 14.0 | 196 | 0.2285 | 0.9492 | 0.9606 | 0.875 |
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- | 0.1636 | 15.0 | 210 | 0.2178 | 0.9667 | 0.9772 | 0.9375 |
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- | 0.1581 | 16.0 | 224 | 0.2110 | 0.9667 | 0.9772 | 0.9375 |
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- | 0.1473 | 17.0 | 238 | 0.2057 | 0.9492 | 0.9606 | 0.875 |
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- | 0.1479 | 18.0 | 252 | 0.2025 | 0.9667 | 0.9772 | 0.9375 |
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- | 0.141 | 19.0 | 266 | 0.2038 | 0.9667 | 0.9772 | 0.9375 |
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- | 0.1424 | 20.0 | 280 | 0.2032 | 0.9667 | 0.9772 | 0.9375 |
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  ### Framework versions
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.9761904761904762
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9545454545454546
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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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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_v2 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2016
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+ - F1: 0.9762
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+ - Roc Auc: 0.9844
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+ - Accuracy: 0.9545
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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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+ | 0.6596 | 1.0 | 16 | 0.6086 | 0.2687 | 0.5474 | 0.0 |
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+ | 0.5448 | 2.0 | 32 | 0.5354 | 0.3824 | 0.6063 | 0.0 |
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+ | 0.5106 | 3.0 | 48 | 0.4874 | 0.4444 | 0.6382 | 0.0455 |
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+ | 0.4353 | 4.0 | 64 | 0.4301 | 0.5352 | 0.6889 | 0.1818 |
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+ | 0.3699 | 5.0 | 80 | 0.3890 | 0.6579 | 0.7640 | 0.3636 |
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+ | 0.349 | 6.0 | 96 | 0.3663 | 0.6667 | 0.7633 | 0.3182 |
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+ | 0.3104 | 7.0 | 112 | 0.3327 | 0.7105 | 0.7953 | 0.4545 |
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+ | 0.3023 | 8.0 | 128 | 0.2971 | 0.7733 | 0.8303 | 0.5455 |
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+ | 0.2676 | 9.0 | 144 | 0.2766 | 0.8395 | 0.8861 | 0.7727 |
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+ | 0.2374 | 10.0 | 160 | 0.2541 | 0.8537 | 0.8980 | 0.7727 |
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+ | 0.2238 | 11.0 | 176 | 0.2399 | 0.9024 | 0.9293 | 0.8182 |
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+ | 0.2084 | 12.0 | 192 | 0.2221 | 0.9286 | 0.9531 | 0.8636 |
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+ | 0.2143 | 13.0 | 208 | 0.2138 | 0.9286 | 0.9531 | 0.8636 |
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+ | 0.1846 | 14.0 | 224 | 0.2016 | 0.9762 | 0.9844 | 0.9545 |
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+ | 0.1812 | 15.0 | 240 | 0.1957 | 0.9762 | 0.9844 | 0.9545 |
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+ | 0.1756 | 16.0 | 256 | 0.1881 | 0.9647 | 0.9806 | 0.9091 |
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+ | 0.1662 | 17.0 | 272 | 0.1845 | 0.9762 | 0.9844 | 0.9545 |
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+ | 0.1715 | 18.0 | 288 | 0.1802 | 0.9762 | 0.9844 | 0.9545 |
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+ | 0.1585 | 19.0 | 304 | 0.1782 | 0.9762 | 0.9844 | 0.9545 |
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+ | 0.1595 | 20.0 | 320 | 0.1775 | 0.9762 | 0.9844 | 0.9545 |
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  ### Framework versions