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bert-base-cased-NER-favsbot

Token Classification
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PyTorch
bert
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update model card README.md

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  1. README.md +27 -27
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@@ -24,13 +24,13 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8275862068965517
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  - name: Recall
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  type: recall
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- value: 0.96
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  - name: F1
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  type: f1
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- value: 0.888888888888889
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  - name: Accuracy
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  type: accuracy
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  value: 0.9444444444444444
@@ -43,10 +43,10 @@ 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 favsbot dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1790
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- - Precision: 0.8276
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- - Recall: 0.96
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- - F1: 0.8889
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  - Accuracy: 0.9444
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  ## Model description
@@ -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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- | No log | 1.0 | 7 | 1.6969 | 0.0 | 0.0 | 0.0 | 0.5833 |
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- | No log | 2.0 | 14 | 1.1718 | 0.6667 | 0.08 | 0.1429 | 0.625 |
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- | No log | 3.0 | 21 | 0.8695 | 0.6111 | 0.44 | 0.5116 | 0.75 |
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- | No log | 4.0 | 28 | 0.6593 | 0.6923 | 0.72 | 0.7059 | 0.8333 |
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- | No log | 5.0 | 35 | 0.4980 | 0.8077 | 0.84 | 0.8235 | 0.8889 |
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- | No log | 6.0 | 42 | 0.3915 | 0.7667 | 0.92 | 0.8364 | 0.9167 |
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- | No log | 7.0 | 49 | 0.3162 | 0.8 | 0.96 | 0.8727 | 0.9306 |
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- | No log | 8.0 | 56 | 0.2614 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 9.0 | 63 | 0.2375 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 10.0 | 70 | 0.2253 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 11.0 | 77 | 0.2067 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 12.0 | 84 | 0.1950 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 13.0 | 91 | 0.1913 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 14.0 | 98 | 0.1898 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 15.0 | 105 | 0.1878 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 16.0 | 112 | 0.1851 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 17.0 | 119 | 0.1829 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 18.0 | 126 | 0.1818 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 19.0 | 133 | 0.1801 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 20.0 | 140 | 0.1790 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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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: 0.8461538461538461
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  - name: Recall
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  type: recall
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+ value: 0.88
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  - name: F1
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  type: f1
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+ value: 0.8627450980392156
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  - name: Accuracy
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  type: accuracy
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  value: 0.9444444444444444
 
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1680
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+ - Precision: 0.8462
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+ - Recall: 0.88
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+ - F1: 0.8627
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  - Accuracy: 0.9444
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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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+ | No log | 1.0 | 7 | 1.8761 | 0.0 | 0.0 | 0.0 | 0.5833 |
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+ | No log | 2.0 | 14 | 1.3530 | 0.0 | 0.0 | 0.0 | 0.5972 |
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+ | No log | 3.0 | 21 | 1.0400 | 1.0 | 0.12 | 0.2143 | 0.6389 |
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+ | No log | 4.0 | 28 | 0.7987 | 0.7895 | 0.6 | 0.6818 | 0.8194 |
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+ | No log | 5.0 | 35 | 0.6055 | 0.85 | 0.68 | 0.7556 | 0.875 |
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+ | No log | 6.0 | 42 | 0.4749 | 0.8696 | 0.8 | 0.8333 | 0.9167 |
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+ | No log | 7.0 | 49 | 0.3838 | 0.84 | 0.84 | 0.8400 | 0.9444 |
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+ | No log | 8.0 | 56 | 0.3084 | 0.88 | 0.88 | 0.88 | 0.9583 |
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+ | No log | 9.0 | 63 | 0.2643 | 0.88 | 0.88 | 0.88 | 0.9583 |
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+ | No log | 10.0 | 70 | 0.2360 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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+ | No log | 11.0 | 77 | 0.2168 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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+ | No log | 12.0 | 84 | 0.2031 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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+ | No log | 13.0 | 91 | 0.1937 | 0.88 | 0.88 | 0.88 | 0.9583 |
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+ | No log | 14.0 | 98 | 0.1853 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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+ | No log | 15.0 | 105 | 0.1791 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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+ | No log | 16.0 | 112 | 0.1757 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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+ | No log | 17.0 | 119 | 0.1718 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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+ | No log | 18.0 | 126 | 0.1698 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
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+ | No log | 19.0 | 133 | 0.1686 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
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+ | No log | 20.0 | 140 | 0.1680 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
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  ### Framework versions