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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
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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: 0.9370212765957446
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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
@@ -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: 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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@@ -78,16 +78,16 @@ 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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- | 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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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.7532580364900087
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  - name: Recall
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  type: recall
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+ value: 0.7416595380667237
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  - name: F1
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  type: f1
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+ value: 0.7474137931034481
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9492845117845118
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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: 0.2432
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+ - Precision: 0.7533
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+ - Recall: 0.7417
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+ - F1: 0.7474
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+ - Accuracy: 0.9493
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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 | 0.3950 | 0.5892 | 0.4380 | 0.5025 | 0.9104 |
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+ | 0.5722 | 2.0 | 522 | 0.2869 | 0.6306 | 0.6484 | 0.6394 | 0.9311 |
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+ | 0.5722 | 3.0 | 783 | 0.2300 | 0.7047 | 0.6758 | 0.6900 | 0.9452 |
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+ | 0.2424 | 4.0 | 1044 | 0.2293 | 0.6793 | 0.7340 | 0.7056 | 0.9426 |
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+ | 0.2424 | 5.0 | 1305 | 0.2208 | 0.7952 | 0.7074 | 0.7488 | 0.9497 |
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+ | 0.1564 | 6.0 | 1566 | 0.2345 | 0.7104 | 0.7408 | 0.7253 | 0.9447 |
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+ | 0.1564 | 7.0 | 1827 | 0.2312 | 0.6956 | 0.7605 | 0.7266 | 0.9456 |
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+ | 0.112 | 8.0 | 2088 | 0.2404 | 0.7673 | 0.7417 | 0.7542 | 0.9500 |
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+ | 0.112 | 9.0 | 2349 | 0.2303 | 0.7698 | 0.7553 | 0.7625 | 0.9531 |
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+ | 0.0879 | 10.0 | 2610 | 0.2432 | 0.7533 | 0.7417 | 0.7474 | 0.9493 |
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  ### Framework versions