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favs_token_classification_v2_updated_data

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results
Inference Endpoints
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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.6626506024096386
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  - name: Recall
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  type: recall
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- value: 0.8270676691729323
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  - name: F1
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  type: f1
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- value: 0.7357859531772575
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  - name: Accuracy
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  type: accuracy
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- value: 0.8454810495626822
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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
@@ -43,11 +43,11 @@ 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 token_classification_v2 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5552
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- - Precision: 0.6627
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- - Recall: 0.8271
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- - F1: 0.7358
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- - Accuracy: 0.8455
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  ## Model description
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@@ -78,26 +78,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 2.2341 | 1.0 | 12 | 1.9985 | 0.25 | 0.0526 | 0.0870 | 0.2974 |
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- | 1.9965 | 2.0 | 24 | 1.7621 | 0.4066 | 0.2782 | 0.3304 | 0.3965 |
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- | 1.8046 | 3.0 | 36 | 1.5089 | 0.3444 | 0.3910 | 0.3662 | 0.5131 |
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- | 1.6799 | 4.0 | 48 | 1.2935 | 0.3859 | 0.5338 | 0.4479 | 0.6239 |
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- | 1.3075 | 5.0 | 60 | 1.1264 | 0.4350 | 0.5789 | 0.4968 | 0.6618 |
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- | 1.1735 | 6.0 | 72 | 0.9940 | 0.4780 | 0.6541 | 0.5524 | 0.6939 |
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- | 1.0608 | 7.0 | 84 | 0.8906 | 0.4917 | 0.6692 | 0.5669 | 0.7230 |
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- | 0.9299 | 8.0 | 96 | 0.8121 | 0.5225 | 0.6992 | 0.5981 | 0.7493 |
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- | 0.8562 | 9.0 | 108 | 0.7539 | 0.5740 | 0.7293 | 0.6424 | 0.7813 |
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- | 0.7132 | 10.0 | 120 | 0.6918 | 0.5952 | 0.7519 | 0.6645 | 0.8017 |
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- | 0.6637 | 11.0 | 132 | 0.6684 | 0.5965 | 0.7669 | 0.6711 | 0.8076 |
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- | 0.6209 | 12.0 | 144 | 0.6381 | 0.5906 | 0.7594 | 0.6645 | 0.8047 |
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- | 0.5874 | 13.0 | 156 | 0.6100 | 0.625 | 0.7895 | 0.6977 | 0.8222 |
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- | 0.5685 | 14.0 | 168 | 0.5890 | 0.6506 | 0.8120 | 0.7224 | 0.8338 |
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- | 0.498 | 15.0 | 180 | 0.5801 | 0.6310 | 0.7970 | 0.7043 | 0.8280 |
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- | 0.4818 | 16.0 | 192 | 0.5743 | 0.6391 | 0.8120 | 0.7152 | 0.8338 |
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- | 0.4637 | 17.0 | 204 | 0.5707 | 0.6391 | 0.8120 | 0.7152 | 0.8338 |
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- | 0.4523 | 18.0 | 216 | 0.5627 | 0.6488 | 0.8195 | 0.7243 | 0.8367 |
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- | 0.477 | 19.0 | 228 | 0.5569 | 0.6527 | 0.8195 | 0.7267 | 0.8397 |
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- | 0.4287 | 20.0 | 240 | 0.5552 | 0.6627 | 0.8271 | 0.7358 | 0.8455 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.6923076923076923
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  - name: Recall
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  type: recall
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+ value: 0.8357142857142857
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  - name: F1
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  type: f1
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+ value: 0.7572815533980584
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8493150684931506
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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 token_classification_v2 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5346
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+ - Precision: 0.6923
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+ - Recall: 0.8357
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+ - F1: 0.7573
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+ - Accuracy: 0.8493
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 2.3096 | 1.0 | 13 | 1.9927 | 0.3011 | 0.2 | 0.2403 | 0.3726 |
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+ | 2.038 | 2.0 | 26 | 1.7093 | 0.2569 | 0.2643 | 0.2606 | 0.4274 |
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+ | 1.8391 | 3.0 | 39 | 1.4452 | 0.3057 | 0.4214 | 0.3544 | 0.5562 |
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+ | 1.4912 | 4.0 | 52 | 1.2176 | 0.4130 | 0.5429 | 0.4691 | 0.6493 |
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+ | 1.3296 | 5.0 | 65 | 1.0368 | 0.4973 | 0.6643 | 0.5688 | 0.7123 |
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+ | 1.2036 | 6.0 | 78 | 0.9084 | 0.5053 | 0.6786 | 0.5793 | 0.7260 |
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+ | 0.9244 | 7.0 | 91 | 0.8148 | 0.5543 | 0.7286 | 0.6296 | 0.7616 |
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+ | 0.8293 | 8.0 | 104 | 0.7482 | 0.5698 | 0.7286 | 0.6395 | 0.7726 |
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+ | 0.7422 | 9.0 | 117 | 0.6961 | 0.5833 | 0.75 | 0.6562 | 0.7836 |
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+ | 0.6379 | 10.0 | 130 | 0.6613 | 0.6124 | 0.7786 | 0.6855 | 0.8027 |
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+ | 0.6071 | 11.0 | 143 | 0.6357 | 0.6193 | 0.7786 | 0.6899 | 0.8082 |
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+ | 0.5526 | 12.0 | 156 | 0.6033 | 0.6433 | 0.7857 | 0.7074 | 0.8164 |
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+ | 0.537 | 13.0 | 169 | 0.5813 | 0.6512 | 0.8 | 0.7179 | 0.8301 |
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+ | 0.4806 | 14.0 | 182 | 0.5706 | 0.6608 | 0.8071 | 0.7267 | 0.8329 |
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+ | 0.4503 | 15.0 | 195 | 0.5594 | 0.6647 | 0.8071 | 0.7290 | 0.8356 |
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+ | 0.4149 | 16.0 | 208 | 0.5503 | 0.6805 | 0.8214 | 0.7443 | 0.8438 |
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+ | 0.4175 | 17.0 | 221 | 0.5430 | 0.6824 | 0.8286 | 0.7484 | 0.8438 |
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+ | 0.4337 | 18.0 | 234 | 0.5396 | 0.6923 | 0.8357 | 0.7573 | 0.8493 |
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+ | 0.3965 | 19.0 | 247 | 0.5361 | 0.6882 | 0.8357 | 0.7548 | 0.8493 |
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+ | 0.3822 | 20.0 | 260 | 0.5346 | 0.6923 | 0.8357 | 0.7573 | 0.8493 |
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  ### Framework versions