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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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- lg-ner |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: luganda-ner-v6 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: lg-ner |
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type: lg-ner |
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config: lug |
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split: test |
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args: lug |
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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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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 [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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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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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- 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 |
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