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Conrad747
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luganda-ner-v3

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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@@ -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: 0.7824620573355818
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  - name: Recall
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  type: recall
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- value: 0.7938408896492729
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  - name: F1
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  type: f1
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- value: 0.7881104033970276
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  - name: Accuracy
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  type: accuracy
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- value: 0.9543876262626263
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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-large](https://huggingface.co/xlm-roberta-large) on the lg-ner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2106
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- - Precision: 0.7825
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- - Recall: 0.7938
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- - F1: 0.7881
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- - Accuracy: 0.9544
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  ## Model description
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@@ -78,17 +78,17 @@ 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 | 0.3026 | 0.7257 | 0.5047 | 0.5954 | 0.9270 |
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- | 0.4863 | 2.0 | 522 | 0.2362 | 0.7214 | 0.6801 | 0.7001 | 0.9426 |
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- | 0.4863 | 3.0 | 783 | 0.2053 | 0.7226 | 0.7622 | 0.7419 | 0.9474 |
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- | 0.1814 | 4.0 | 1044 | 0.2115 | 0.7081 | 0.7802 | 0.7424 | 0.9465 |
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- | 0.1814 | 5.0 | 1305 | 0.1974 | 0.7850 | 0.7964 | 0.7907 | 0.9568 |
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- | 0.1011 | 6.0 | 1566 | 0.2106 | 0.7825 | 0.7938 | 0.7881 | 0.9544 |
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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: 0.8141289437585734
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  - name: Recall
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  type: recall
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+ value: 0.7971793149764943
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  - name: F1
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  type: f1
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+ value: 0.8055649813369528
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  - name: Accuracy
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  type: accuracy
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+ value: 0.952700740525628
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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-large](https://huggingface.co/xlm-roberta-large) on the lg-ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2295
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+ - Precision: 0.8141
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+ - Recall: 0.7972
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+ - F1: 0.8056
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+ - Accuracy: 0.9527
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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 | 0.4226 | 0.6273 | 0.3606 | 0.4580 | 0.8928 |
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+ | 0.5572 | 2.0 | 522 | 0.2835 | 0.7720 | 0.6185 | 0.6868 | 0.9219 |
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+ | 0.5572 | 3.0 | 783 | 0.2740 | 0.7579 | 0.7401 | 0.7489 | 0.9311 |
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+ | 0.1745 | 4.0 | 1044 | 0.2423 | 0.7895 | 0.7683 | 0.7788 | 0.9399 |
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+ | 0.1745 | 5.0 | 1305 | 0.2273 | 0.8048 | 0.7945 | 0.7996 | 0.9498 |
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+ | 0.086 | 6.0 | 1566 | 0.2295 | 0.8141 | 0.7972 | 0.8056 | 0.9527 |
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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