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bert-base-cased-NER-favsbot

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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: 0.8275862068965517
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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
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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 favsbot dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1859
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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
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@@ -78,31 +78,31 @@ 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 | 1.8583 | 0.0 | 0.0 | 0.0 | 0.5972 |
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- | No log | 2.0 | 14 | 1.2973 | 0.0 | 0.0 | 0.0 | 0.5972 |
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- | No log | 3.0 | 21 | 0.9560 | 0.6 | 0.36 | 0.45 | 0.75 |
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- | No log | 4.0 | 28 | 0.7307 | 0.75 | 0.6 | 0.6667 | 0.8194 |
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- | No log | 5.0 | 35 | 0.5585 | 0.8261 | 0.76 | 0.7917 | 0.8611 |
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- | No log | 6.0 | 42 | 0.4430 | 0.7931 | 0.92 | 0.8519 | 0.9306 |
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- | No log | 7.0 | 49 | 0.3628 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 8.0 | 56 | 0.2887 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 9.0 | 63 | 0.2601 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 10.0 | 70 | 0.2398 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 11.0 | 77 | 0.2169 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 12.0 | 84 | 0.2034 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 13.0 | 91 | 0.1998 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 14.0 | 98 | 0.1983 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 15.0 | 105 | 0.1928 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 16.0 | 112 | 0.1895 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 17.0 | 119 | 0.1891 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 18.0 | 126 | 0.1876 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 19.0 | 133 | 0.1867 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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- | No log | 20.0 | 140 | 0.1859 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
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  ### Framework versions
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- - Transformers 4.23.1
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- - Pytorch 1.12.1+cu113
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  - Datasets 2.6.1
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- - Tokenizers 0.13.1
 
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  metrics:
25
  - name: Precision
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  type: precision
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+ value: 0.8571428571428571
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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: 0.9056603773584904
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9583333333333334
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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 favsbot dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0992
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+ - Precision: 0.8571
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  - Recall: 0.96
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+ - F1: 0.9057
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+ - Accuracy: 0.9583
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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 | 10 | 1.7643 | 0.0 | 0.0 | 0.0 | 0.5694 |
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+ | No log | 2.0 | 20 | 1.1420 | 0.0 | 0.0 | 0.0 | 0.5833 |
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+ | No log | 3.0 | 30 | 0.7946 | 0.9375 | 0.6 | 0.7317 | 0.8056 |
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+ | No log | 4.0 | 40 | 0.5625 | 0.8182 | 0.72 | 0.7660 | 0.8611 |
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+ | No log | 5.0 | 50 | 0.4217 | 0.8148 | 0.88 | 0.8462 | 0.9306 |
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+ | No log | 6.0 | 60 | 0.3082 | 0.8519 | 0.92 | 0.8846 | 0.9444 |
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+ | No log | 7.0 | 70 | 0.2386 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
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+ | No log | 8.0 | 80 | 0.1965 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
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+ | No log | 9.0 | 90 | 0.1626 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
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+ | No log | 10.0 | 100 | 0.1465 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 11.0 | 110 | 0.1314 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 12.0 | 120 | 0.1215 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 13.0 | 130 | 0.1160 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 14.0 | 140 | 0.1104 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 15.0 | 150 | 0.1050 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 16.0 | 160 | 0.1012 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 17.0 | 170 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 18.0 | 180 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 19.0 | 190 | 0.0995 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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+ | No log | 20.0 | 200 | 0.0992 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
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  ### Framework versions
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+ - Transformers 4.21.1
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+ - Pytorch 1.12.1
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  - Datasets 2.6.1
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+ - Tokenizers 0.12.1