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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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  ---
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  license: mit
 
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  tags:
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  - generated_from_trainer
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  datasets:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8241451500348919
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  - name: Recall
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  type: recall
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- value: 0.7931497649429147
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  - name: F1
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  type: f1
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- value: 0.8083504449007528
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  - name: Accuracy
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  type: accuracy
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- value: 0.9525918396979817
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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
@@ -41,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # luganda-ner-v6
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- This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the lg-ner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2417
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- - Precision: 0.8241
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- - Recall: 0.7931
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- - F1: 0.8084
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- - Accuracy: 0.9526
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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 | 0.4290 | 0.5281 | 0.3096 | 0.3903 | 0.8864 |
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- | 0.5483 | 2.0 | 522 | 0.2873 | 0.7307 | 0.5776 | 0.6452 | 0.9216 |
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- | 0.5483 | 3.0 | 783 | 0.2482 | 0.7745 | 0.6783 | 0.7232 | 0.9334 |
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- | 0.1931 | 4.0 | 1044 | 0.2472 | 0.7671 | 0.6991 | 0.7316 | 0.9360 |
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- | 0.1931 | 5.0 | 1305 | 0.2425 | 0.8053 | 0.7388 | 0.7706 | 0.9433 |
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- | 0.1016 | 6.0 | 1566 | 0.2157 | 0.8253 | 0.7710 | 0.7972 | 0.9490 |
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- | 0.1016 | 7.0 | 1827 | 0.2332 | 0.8161 | 0.7717 | 0.7932 | 0.9501 |
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- | 0.0654 | 8.0 | 2088 | 0.2375 | 0.8312 | 0.7804 | 0.8050 | 0.9514 |
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- | 0.0654 | 9.0 | 2349 | 0.2367 | 0.8309 | 0.7884 | 0.8091 | 0.9528 |
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- | 0.047 | 10.0 | 2610 | 0.2417 | 0.8241 | 0.7931 | 0.8084 | 0.9526 |
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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
 
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  ---
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  license: mit
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+ base_model: xlm-roberta-base
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  tags:
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  - generated_from_trainer
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  datasets:
 
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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
 
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  # luganda-ner-v6
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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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  | 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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+ - Transformers 4.34.1
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1