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Conrad747
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luganda-ner-v1

Token Classification
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PyTorch
xlm-roberta
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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: 0.7532580364900087
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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
@@ -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: 0.2432
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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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@@ -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 | 0.3950 | 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
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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: 0.7958904109589041
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  - name: Recall
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  type: recall
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+ value: 0.7803895231699127
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  - name: F1
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  type: f1
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+ value: 0.7880637504238724
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9450776825903877
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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: 0.2863
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+ - Precision: 0.7959
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+ - Recall: 0.7804
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+ - F1: 0.7881
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+ - Accuracy: 0.9451
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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 | 0.5454 | 0.4946 | 0.2471 | 0.3296 | 0.8597 |
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+ | 0.6168 | 2.0 | 522 | 0.3598 | 0.6993 | 0.5608 | 0.6224 | 0.9078 |
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+ | 0.6168 | 3.0 | 783 | 0.3842 | 0.6326 | 0.6696 | 0.6506 | 0.8941 |
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+ | 0.2599 | 4.0 | 1044 | 0.2755 | 0.7857 | 0.6870 | 0.7331 | 0.9333 |
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+ | 0.2599 | 5.0 | 1305 | 0.2642 | 0.8019 | 0.7206 | 0.7591 | 0.9351 |
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+ | 0.1498 | 6.0 | 1566 | 0.2755 | 0.7886 | 0.7589 | 0.7734 | 0.9385 |
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+ | 0.1498 | 7.0 | 1827 | 0.2601 | 0.7945 | 0.7609 | 0.7774 | 0.9458 |
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+ | 0.1023 | 8.0 | 2088 | 0.2889 | 0.7875 | 0.7717 | 0.7795 | 0.9409 |
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+ | 0.1023 | 9.0 | 2349 | 0.2819 | 0.8082 | 0.7670 | 0.7870 | 0.9460 |
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+ | 0.0716 | 10.0 | 2610 | 0.2863 | 0.7959 | 0.7804 | 0.7881 | 0.9451 |
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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