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Conrad747
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luganda-ner-v6

Token Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +19 -19
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.7679892400806994
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  - name: Recall
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  type: recall
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- value: 0.7669576897246474
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  - name: F1
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  type: f1
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- value: 0.7674731182795699
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  - name: Accuracy
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  type: accuracy
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- value: 0.9394874401045448
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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
@@ -44,11 +44,11 @@ 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: 0.2845
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- - Precision: 0.7680
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- - Recall: 0.7670
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- - F1: 0.7675
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- - Accuracy: 0.9395
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  ## Model description
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@@ -79,16 +79,16 @@ 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 | 0.6034 | 0.4624 | 0.2149 | 0.2934 | 0.8369 |
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- | 0.6707 | 2.0 | 522 | 0.4082 | 0.7214 | 0.4453 | 0.5507 | 0.8948 |
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- | 0.6707 | 3.0 | 783 | 0.3172 | 0.7413 | 0.6179 | 0.6740 | 0.9181 |
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- | 0.295 | 4.0 | 1044 | 0.3241 | 0.7305 | 0.6810 | 0.7049 | 0.9124 |
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- | 0.295 | 5.0 | 1305 | 0.2784 | 0.7241 | 0.7173 | 0.7206 | 0.9271 |
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- | 0.1781 | 6.0 | 1566 | 0.2703 | 0.7643 | 0.7381 | 0.7509 | 0.9331 |
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- | 0.1781 | 7.0 | 1827 | 0.2585 | 0.7865 | 0.7670 | 0.7766 | 0.9418 |
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- | 0.1172 | 8.0 | 2088 | 0.2696 | 0.8109 | 0.7488 | 0.7786 | 0.9420 |
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- | 0.1172 | 9.0 | 2349 | 0.2680 | 0.7792 | 0.7535 | 0.7661 | 0.9422 |
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- | 0.0874 | 10.0 | 2610 | 0.2845 | 0.7680 | 0.7670 | 0.7675 | 0.9395 |
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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: 0.8029689608636977
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  - name: Recall
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  type: recall
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+ value: 0.7991940899932841
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  - name: F1
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  type: f1
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+ value: 0.8010770784247729
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9467474952809641
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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: 0.2811
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+ - Precision: 0.8030
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+ - Recall: 0.7992
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+ - F1: 0.8011
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+ - Accuracy: 0.9467
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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 | 0.5150 | 0.4947 | 0.2841 | 0.3609 | 0.8692 |
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+ | 0.6193 | 2.0 | 522 | 0.3422 | 0.7491 | 0.5393 | 0.6271 | 0.9161 |
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+ | 0.6193 | 3.0 | 783 | 0.2737 | 0.7744 | 0.6595 | 0.7124 | 0.9306 |
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+ | 0.2505 | 4.0 | 1044 | 0.3201 | 0.7343 | 0.7072 | 0.7205 | 0.9141 |
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+ | 0.2505 | 5.0 | 1305 | 0.2564 | 0.7887 | 0.7569 | 0.7724 | 0.9375 |
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+ | 0.1474 | 6.0 | 1566 | 0.2461 | 0.8173 | 0.7569 | 0.7859 | 0.9459 |
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+ | 0.1474 | 7.0 | 1827 | 0.2739 | 0.8004 | 0.7757 | 0.7879 | 0.9434 |
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+ | 0.0956 | 8.0 | 2088 | 0.2566 | 0.8100 | 0.7905 | 0.8001 | 0.9486 |
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+ | 0.0956 | 9.0 | 2349 | 0.2709 | 0.7859 | 0.7938 | 0.7898 | 0.9463 |
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+ | 0.0712 | 10.0 | 2610 | 0.2811 | 0.8030 | 0.7992 | 0.8011 | 0.9467 |
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  ### Framework versions
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