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luganda-ner-v2

Token Classification
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PyTorch
roberta
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Eval Results (legacy)
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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: 0.79182156133829
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  - name: Recall
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  type: recall
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- value: 0.7842415316642121
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  - name: F1
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  type: f1
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- value: 0.788013318534961
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  - name: Accuracy
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  type: accuracy
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- value: 0.9559346774929295
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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 [roberta-base](https://huggingface.co/roberta-base) on the lg-ner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3199
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- - Precision: 0.7918
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- - Recall: 0.7842
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- - F1: 0.7880
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- - Accuracy: 0.9559
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  ## Model description
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@@ -78,21 +78,21 @@ 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 | 261 | 0.2380 | 0.7942 | 0.7106 | 0.7501 | 0.9526 |
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- | 0.0954 | 2.0 | 522 | 0.2345 | 0.7954 | 0.7872 | 0.7913 | 0.9558 |
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- | 0.0954 | 3.0 | 783 | 0.2560 | 0.8168 | 0.7518 | 0.7830 | 0.9555 |
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- | 0.0562 | 4.0 | 1044 | 0.2815 | 0.7261 | 0.7791 | 0.7517 | 0.9477 |
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- | 0.0562 | 5.0 | 1305 | 0.2738 | 0.7744 | 0.8012 | 0.7875 | 0.9566 |
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- | 0.0345 | 6.0 | 1566 | 0.2951 | 0.8083 | 0.7732 | 0.7904 | 0.9556 |
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- | 0.0345 | 7.0 | 1827 | 0.3026 | 0.7741 | 0.7872 | 0.7806 | 0.9547 |
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- | 0.0215 | 8.0 | 2088 | 0.3062 | 0.8159 | 0.7636 | 0.7889 | 0.9563 |
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- | 0.0215 | 9.0 | 2349 | 0.3157 | 0.7959 | 0.7813 | 0.7886 | 0.9563 |
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- | 0.017 | 10.0 | 2610 | 0.3199 | 0.7918 | 0.7842 | 0.7880 | 0.9559 |
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  ### Framework versions
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- - Transformers 4.26.1
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  - Pytorch 1.13.1+cu116
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- - Datasets 2.10.1
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  - Tokenizers 0.13.2
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.7704421562689279
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  - name: Recall
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  type: recall
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+ value: 0.7695099818511797
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  - name: F1
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  type: f1
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+ value: 0.7699757869249395
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9434371807967313
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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 [roberta-base](https://huggingface.co/roberta-base) on the lg-ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2829
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+ - Precision: 0.7704
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+ - Recall: 0.7695
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+ - F1: 0.7700
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+ - Accuracy: 0.9434
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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 | 261 | 0.4835 | 0.5191 | 0.3037 | 0.3832 | 0.8719 |
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+ | 0.5738 | 2.0 | 522 | 0.3454 | 0.7288 | 0.5203 | 0.6071 | 0.9117 |
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+ | 0.5738 | 3.0 | 783 | 0.2956 | 0.7752 | 0.6612 | 0.7137 | 0.9235 |
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+ | 0.2549 | 4.0 | 1044 | 0.2791 | 0.7537 | 0.6848 | 0.7176 | 0.9258 |
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+ | 0.2549 | 5.0 | 1305 | 0.2801 | 0.7530 | 0.7211 | 0.7367 | 0.9335 |
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+ | 0.1566 | 6.0 | 1566 | 0.2675 | 0.7956 | 0.7229 | 0.7575 | 0.9393 |
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+ | 0.1566 | 7.0 | 1827 | 0.2610 | 0.7744 | 0.7350 | 0.7542 | 0.9423 |
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+ | 0.1054 | 8.0 | 2088 | 0.2731 | 0.7614 | 0.7586 | 0.7600 | 0.9423 |
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+ | 0.1054 | 9.0 | 2349 | 0.2763 | 0.7794 | 0.7526 | 0.7658 | 0.9434 |
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+ | 0.0771 | 10.0 | 2610 | 0.2829 | 0.7704 | 0.7695 | 0.7700 | 0.9434 |
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  ### Framework versions
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+ - Transformers 4.27.4
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  - Pytorch 1.13.1+cu116
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+ - Datasets 2.11.0
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  - Tokenizers 0.13.2