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bert-base-cased-NER-favsbot

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bert
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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: 0.7724867724867724
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  - name: Recall
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  type: recall
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- value: 0.7564766839378239
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  - name: F1
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  type: f1
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- value: 0.7643979057591622
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  - name: Accuracy
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  type: accuracy
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- value: 0.8783185840707964
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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: 0.4287
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- - Precision: 0.7725
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- - Recall: 0.7565
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- - F1: 0.7644
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- - Accuracy: 0.8783
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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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- | No log | 1.0 | 4 | 2.0957 | 0.2823 | 0.3057 | 0.2935 | 0.5 |
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- | No log | 2.0 | 8 | 1.8249 | 0.3253 | 0.1399 | 0.1957 | 0.5044 |
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- | No log | 3.0 | 12 | 1.5957 | 0.3478 | 0.0829 | 0.1339 | 0.4735 |
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- | No log | 4.0 | 16 | 1.4233 | 0.5854 | 0.1244 | 0.2051 | 0.5177 |
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- | No log | 5.0 | 20 | 1.2765 | 0.6923 | 0.2332 | 0.3488 | 0.5796 |
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- | No log | 6.0 | 24 | 1.1334 | 0.7094 | 0.4301 | 0.5355 | 0.6881 |
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- | No log | 7.0 | 28 | 1.0104 | 0.7070 | 0.5751 | 0.6343 | 0.7566 |
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- | No log | 8.0 | 32 | 0.9077 | 0.7 | 0.6166 | 0.6556 | 0.7765 |
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- | No log | 9.0 | 36 | 0.8196 | 0.6977 | 0.6218 | 0.6575 | 0.7832 |
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- | No log | 10.0 | 40 | 0.7435 | 0.7143 | 0.6477 | 0.6793 | 0.7965 |
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- | No log | 11.0 | 44 | 0.6775 | 0.7386 | 0.6736 | 0.7046 | 0.8119 |
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- | No log | 12.0 | 48 | 0.6219 | 0.7657 | 0.6943 | 0.7283 | 0.8230 |
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- | No log | 13.0 | 52 | 0.5737 | 0.7740 | 0.7098 | 0.7405 | 0.8363 |
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- | No log | 14.0 | 56 | 0.5331 | 0.7582 | 0.7150 | 0.736 | 0.8473 |
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- | No log | 15.0 | 60 | 0.5006 | 0.7568 | 0.7254 | 0.7407 | 0.8540 |
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- | No log | 16.0 | 64 | 0.4740 | 0.7622 | 0.7306 | 0.7460 | 0.8628 |
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- | No log | 17.0 | 68 | 0.4545 | 0.7730 | 0.7409 | 0.7566 | 0.8673 |
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- | No log | 18.0 | 72 | 0.4405 | 0.7742 | 0.7461 | 0.7599 | 0.8717 |
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- | No log | 19.0 | 76 | 0.4319 | 0.7713 | 0.7513 | 0.7612 | 0.8761 |
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- | No log | 20.0 | 80 | 0.4287 | 0.7725 | 0.7565 | 0.7644 | 0.8783 |
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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: 0.7537688442211056
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  - name: Recall
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  type: recall
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+ value: 0.7772020725388601
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  - name: F1
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  type: f1
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+ value: 0.7653061224489796
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8960176991150443
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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: 0.4316
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+ - Precision: 0.7538
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+ - Recall: 0.7772
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+ - F1: 0.7653
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+ - Accuracy: 0.8960
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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 | 4 | 1.9796 | 0.25 | 0.0259 | 0.0469 | 0.4248 |
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+ | No log | 2.0 | 8 | 1.7317 | 0.1875 | 0.0155 | 0.0287 | 0.4270 |
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+ | No log | 3.0 | 12 | 1.5312 | 0.28 | 0.0363 | 0.0642 | 0.4779 |
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+ | No log | 4.0 | 16 | 1.3740 | 0.5854 | 0.1244 | 0.2051 | 0.5398 |
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+ | No log | 5.0 | 20 | 1.2446 | 0.5789 | 0.2280 | 0.3271 | 0.5973 |
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+ | No log | 6.0 | 24 | 1.1283 | 0.6016 | 0.3990 | 0.4798 | 0.6792 |
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+ | No log | 7.0 | 28 | 1.0226 | 0.5660 | 0.4663 | 0.5114 | 0.6991 |
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+ | No log | 8.0 | 32 | 0.9234 | 0.5818 | 0.4974 | 0.5363 | 0.7257 |
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+ | No log | 9.0 | 36 | 0.8341 | 0.6071 | 0.5285 | 0.5651 | 0.7478 |
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+ | No log | 10.0 | 40 | 0.7566 | 0.6437 | 0.5803 | 0.6104 | 0.7743 |
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+ | No log | 11.0 | 44 | 0.6893 | 0.6497 | 0.5959 | 0.6216 | 0.7920 |
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+ | No log | 12.0 | 48 | 0.6308 | 0.6667 | 0.6114 | 0.6378 | 0.8075 |
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+ | No log | 13.0 | 52 | 0.5800 | 0.6961 | 0.6528 | 0.6738 | 0.8274 |
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+ | No log | 14.0 | 56 | 0.5377 | 0.7249 | 0.7098 | 0.7173 | 0.8540 |
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+ | No log | 15.0 | 60 | 0.5041 | 0.7644 | 0.7565 | 0.7604 | 0.8739 |
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+ | No log | 16.0 | 64 | 0.4773 | 0.7513 | 0.7668 | 0.7590 | 0.8850 |
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+ | No log | 17.0 | 68 | 0.4574 | 0.7525 | 0.7720 | 0.7621 | 0.8894 |
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+ | No log | 18.0 | 72 | 0.4435 | 0.7487 | 0.7720 | 0.7602 | 0.8916 |
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+ | No log | 19.0 | 76 | 0.4349 | 0.7538 | 0.7772 | 0.7653 | 0.8960 |
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+ | No log | 20.0 | 80 | 0.4316 | 0.7538 | 0.7772 | 0.7653 | 0.8960 |
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  ### Framework versions