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Conrad747
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luganda-ner-v1

Token Classification
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PyTorch
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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update model card README.md

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@@ -19,21 +19,21 @@ model-index:
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  name: lg-ner
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  type: lg-ner
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  config: lug
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- split: train
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  args: lug
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.29015544041450775
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  - name: Recall
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  type: recall
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- value: 0.27722772277227725
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  - name: F1
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  type: f1
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- value: 0.2835443037974684
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  - name: Accuracy
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  type: accuracy
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- value: 0.7297843665768194
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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 [xlm-roberta-base](https://huggingface.co/xlm-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: 1.0530
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- - Precision: 0.2902
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- - Recall: 0.2772
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- - F1: 0.2835
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- - Accuracy: 0.7298
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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 | 25 | 1.2878 | 0.0 | 0.0 | 0.0 | 0.7271 |
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- | No log | 2.0 | 50 | 1.2373 | 0.0 | 0.0 | 0.0 | 0.7271 |
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- | No log | 3.0 | 75 | 1.2309 | 0.3542 | 0.1683 | 0.2282 | 0.7244 |
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- | No log | 4.0 | 100 | 1.1505 | 0.2712 | 0.2376 | 0.2533 | 0.7183 |
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- | No log | 5.0 | 125 | 1.1360 | 0.2579 | 0.2426 | 0.25 | 0.7170 |
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- | No log | 6.0 | 150 | 1.0932 | 0.3108 | 0.2277 | 0.2629 | 0.7338 |
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- | No log | 7.0 | 175 | 1.0761 | 0.2989 | 0.2574 | 0.2766 | 0.7298 |
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- | No log | 8.0 | 200 | 1.0645 | 0.2805 | 0.3069 | 0.2931 | 0.7244 |
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- | No log | 9.0 | 225 | 1.0577 | 0.3022 | 0.2723 | 0.2865 | 0.7325 |
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- | No log | 10.0 | 250 | 1.0530 | 0.2902 | 0.2772 | 0.2835 | 0.7298 |
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  ### Framework versions
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- - Transformers 4.24.0
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- - Pytorch 1.12.1+cu113
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- - Datasets 2.7.1
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  - Tokenizers 0.13.2
 
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  name: lg-ner
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  type: lg-ner
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  config: lug
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+ split: test
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  args: lug
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9370212765957446
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  - name: Recall
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  type: recall
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+ value: 0.9359591952394446
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  - name: F1
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  type: f1
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+ value: 0.9364899347887723
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9824210946863764
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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 [xlm-roberta-base](https://huggingface.co/xlm-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: 0.0908
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+ - Precision: 0.9370
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+ - Recall: 0.9360
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+ - F1: 0.9365
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+ - Accuracy: 0.9824
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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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+ | 0.5792 | 1.0 | 609 | 0.2463 | 0.7259 | 0.7662 | 0.7455 | 0.9406 |
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+ | 0.2271 | 2.0 | 1218 | 0.1587 | 0.8198 | 0.8782 | 0.8480 | 0.9607 |
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+ | 0.1652 | 3.0 | 1827 | 0.1289 | 0.8612 | 0.8918 | 0.8762 | 0.9677 |
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+ | 0.1266 | 4.0 | 2436 | 0.1083 | 0.8990 | 0.9059 | 0.9025 | 0.9744 |
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+ | 0.081 | 5.0 | 3045 | 0.1043 | 0.9183 | 0.9147 | 0.9165 | 0.9767 |
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+ | 0.0676 | 6.0 | 3654 | 0.0893 | 0.9261 | 0.9334 | 0.9297 | 0.9811 |
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+ | 0.0565 | 7.0 | 4263 | 0.0877 | 0.9389 | 0.9368 | 0.9379 | 0.9813 |
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+ | 0.0519 | 8.0 | 4872 | 0.0919 | 0.9404 | 0.9340 | 0.9372 | 0.9819 |
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+ | 0.047 | 9.0 | 5481 | 0.0896 | 0.9376 | 0.9360 | 0.9368 | 0.9825 |
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+ | 0.0379 | 10.0 | 6090 | 0.0908 | 0.9370 | 0.9360 | 0.9365 | 0.9824 |
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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