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nguyenkhoa2407
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favs_filter_classification_v2

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results
Inference Endpoints
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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: 1.0
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  - name: Accuracy
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  type: accuracy
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- value: 1.0
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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: 0.2201
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- - F1: 1.0
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- - Roc Auc: 1.0
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- - Accuracy: 1.0
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1.3e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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- | 0.6747 | 1.0 | 13 | 0.5868 | 0.5 | 0.6649 | 0.1818 |
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- | 0.5843 | 2.0 | 26 | 0.5321 | 0.4828 | 0.6575 | 0.0 |
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- | 0.5445 | 3.0 | 39 | 0.5029 | 0.6 | 0.7162 | 0.2727 |
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- | 0.4713 | 4.0 | 52 | 0.4667 | 0.6207 | 0.725 | 0.2727 |
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- | 0.4304 | 5.0 | 65 | 0.4321 | 0.6667 | 0.7575 | 0.4545 |
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- | 0.3983 | 6.0 | 78 | 0.3909 | 0.7500 | 0.8 | 0.4545 |
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- | 0.3433 | 7.0 | 91 | 0.3571 | 0.7879 | 0.825 | 0.5455 |
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- | 0.3186 | 8.0 | 104 | 0.3319 | 0.8235 | 0.85 | 0.6364 |
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- | 0.2967 | 9.0 | 117 | 0.3049 | 0.8571 | 0.875 | 0.6364 |
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- | 0.2739 | 10.0 | 130 | 0.2857 | 0.8571 | 0.875 | 0.6364 |
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- | 0.2535 | 11.0 | 143 | 0.2686 | 0.9474 | 0.95 | 0.8182 |
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- | 0.234 | 12.0 | 156 | 0.2501 | 0.9474 | 0.95 | 0.8182 |
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- | 0.2338 | 13.0 | 169 | 0.2358 | 0.9474 | 0.95 | 0.8182 |
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- | 0.2049 | 14.0 | 182 | 0.2201 | 1.0 | 1.0 | 1.0 |
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- | 0.1942 | 15.0 | 195 | 0.2098 | 1.0 | 1.0 | 1.0 |
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- | 0.1957 | 16.0 | 208 | 0.2063 | 1.0 | 1.0 | 1.0 |
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- | 0.1892 | 17.0 | 221 | 0.1969 | 1.0 | 1.0 | 1.0 |
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- | 0.1836 | 18.0 | 234 | 0.1919 | 1.0 | 1.0 | 1.0 |
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- | 0.1767 | 19.0 | 247 | 0.1909 | 1.0 | 1.0 | 1.0 |
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- | 0.1702 | 20.0 | 260 | 0.1901 | 1.0 | 1.0 | 1.0 |
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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: 0.9666666666666667
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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
 
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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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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1.5e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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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