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favs-filtersort-multilabel-classification-bert-base-cased

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: 0.7714285714285716
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  - name: Accuracy
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  type: accuracy
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- value: 0.4
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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_sort dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2711
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- - F1: 0.7714
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- - Roc Auc: 0.8309
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- - Accuracy: 0.4
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  ## Model description
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@@ -63,37 +63,32 @@ The following hyperparameters were used during training:
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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: 25
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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- | 0.6535 | 1.0 | 12 | 0.5860 | 0.4524 | 0.6444 | 0.0 |
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- | 0.5843 | 2.0 | 24 | 0.5121 | 0.5 | 0.6708 | 0.0 |
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- | 0.5308 | 3.0 | 36 | 0.4460 | 0.5484 | 0.6950 | 0.0 |
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- | 0.4663 | 4.0 | 48 | 0.4023 | 0.5574 | 0.6989 | 0.0 |
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- | 0.4116 | 5.0 | 60 | 0.3769 | 0.5806 | 0.7117 | 0.0 |
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- | 0.3936 | 6.0 | 72 | 0.3620 | 0.6032 | 0.7245 | 0.0 |
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- | 0.3691 | 7.0 | 84 | 0.3519 | 0.625 | 0.7373 | 0.0 |
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- | 0.3565 | 8.0 | 96 | 0.3410 | 0.6269 | 0.7425 | 0.0 |
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- | 0.3548 | 9.0 | 108 | 0.3324 | 0.6562 | 0.7540 | 0.0 |
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- | 0.3235 | 10.0 | 120 | 0.3229 | 0.6866 | 0.7758 | 0.1 |
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- | 0.3157 | 11.0 | 132 | 0.3115 | 0.7164 | 0.7924 | 0.2 |
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- | 0.297 | 12.0 | 144 | 0.3055 | 0.7164 | 0.7924 | 0.2 |
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- | 0.2923 | 13.0 | 156 | 0.2988 | 0.7246 | 0.8014 | 0.2 |
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- | 0.2848 | 14.0 | 168 | 0.2903 | 0.7164 | 0.7924 | 0.2 |
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- | 0.2715 | 15.0 | 180 | 0.2908 | 0.7429 | 0.8142 | 0.3 |
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- | 0.2696 | 16.0 | 192 | 0.2807 | 0.7353 | 0.8052 | 0.3 |
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- | 0.2543 | 17.0 | 204 | 0.2794 | 0.7536 | 0.8181 | 0.3 |
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- | 0.2504 | 18.0 | 216 | 0.2711 | 0.7714 | 0.8309 | 0.4 |
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- | 0.2577 | 19.0 | 228 | 0.2708 | 0.7536 | 0.8181 | 0.3 |
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- | 0.2401 | 20.0 | 240 | 0.2693 | 0.7536 | 0.8181 | 0.3 |
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- | 0.2415 | 21.0 | 252 | 0.2669 | 0.7714 | 0.8309 | 0.4 |
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- | 0.241 | 22.0 | 264 | 0.2691 | 0.7536 | 0.8181 | 0.3 |
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- | 0.2341 | 23.0 | 276 | 0.2669 | 0.7536 | 0.8181 | 0.3 |
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- | 0.2355 | 24.0 | 288 | 0.2660 | 0.7536 | 0.8181 | 0.3 |
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- | 0.232 | 25.0 | 300 | 0.2655 | 0.7536 | 0.8181 | 0.3 |
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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: 0.7428571428571428
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  - name: Accuracy
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  type: accuracy
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+ value: 0.2
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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_sort dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3066
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+ - F1: 0.7429
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+ - Roc Auc: 0.8142
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+ - Accuracy: 0.2
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  ## Model description
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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: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.7601 | 1.0 | 12 | 0.6966 | 0.2564 | 0.4518 | 0.0 |
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+ | 0.6757 | 2.0 | 24 | 0.5629 | 0.6667 | 0.7785 | 0.0 |
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+ | 0.5796 | 3.0 | 36 | 0.4652 | 0.6286 | 0.7477 | 0.0 |
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+ | 0.5026 | 4.0 | 48 | 0.4161 | 0.6479 | 0.7605 | 0.0 |
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+ | 0.4282 | 5.0 | 60 | 0.3830 | 0.6849 | 0.7862 | 0.0 |
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+ | 0.4085 | 6.0 | 72 | 0.3658 | 0.7273 | 0.7962 | 0.0 |
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+ | 0.3847 | 7.0 | 84 | 0.3538 | 0.7353 | 0.8052 | 0.0 |
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+ | 0.3829 | 8.0 | 96 | 0.3457 | 0.6761 | 0.7772 | 0.0 |
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+ | 0.3758 | 9.0 | 108 | 0.3409 | 0.6857 | 0.7810 | 0.0 |
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+ | 0.3487 | 10.0 | 120 | 0.3327 | 0.7143 | 0.7976 | 0.0 |
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+ | 0.3421 | 11.0 | 132 | 0.3268 | 0.6866 | 0.7758 | 0.0 |
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+ | 0.3351 | 12.0 | 144 | 0.3183 | 0.7059 | 0.7886 | 0.0 |
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+ | 0.3245 | 13.0 | 156 | 0.3149 | 0.7246 | 0.8014 | 0.0 |
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+ | 0.3191 | 14.0 | 168 | 0.3087 | 0.7246 | 0.8014 | 0.1 |
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+ | 0.3083 | 15.0 | 180 | 0.3066 | 0.7429 | 0.8142 | 0.2 |
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+ | 0.3061 | 16.0 | 192 | 0.3062 | 0.7429 | 0.8142 | 0.2 |
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+ | 0.2935 | 17.0 | 204 | 0.3017 | 0.7429 | 0.8142 | 0.2 |
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+ | 0.2888 | 18.0 | 216 | 0.3009 | 0.7429 | 0.8142 | 0.2 |
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+ | 0.297 | 19.0 | 228 | 0.3022 | 0.7429 | 0.8142 | 0.2 |
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+ | 0.2868 | 20.0 | 240 | 0.3014 | 0.7429 | 0.8142 | 0.2 |
 
 
 
 
 
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  ### Framework versions