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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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- 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-v2 |
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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: 0.7704421562689279 |
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- name: Recall |
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type: recall |
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value: 0.7695099818511797 |
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- name: F1 |
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type: f1 |
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value: 0.7699757869249395 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9434371807967313 |
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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-v2 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/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: 0.2829 |
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- Precision: 0.7704 |
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- Recall: 0.7695 |
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- F1: 0.7700 |
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- Accuracy: 0.9434 |
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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 | 0.4835 | 0.5191 | 0.3037 | 0.3832 | 0.8719 | |
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| 0.5738 | 2.0 | 522 | 0.3454 | 0.7288 | 0.5203 | 0.6071 | 0.9117 | |
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| 0.5738 | 3.0 | 783 | 0.2956 | 0.7752 | 0.6612 | 0.7137 | 0.9235 | |
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| 0.2549 | 4.0 | 1044 | 0.2791 | 0.7537 | 0.6848 | 0.7176 | 0.9258 | |
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| 0.2549 | 5.0 | 1305 | 0.2801 | 0.7530 | 0.7211 | 0.7367 | 0.9335 | |
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| 0.1566 | 6.0 | 1566 | 0.2675 | 0.7956 | 0.7229 | 0.7575 | 0.9393 | |
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| 0.1566 | 7.0 | 1827 | 0.2610 | 0.7744 | 0.7350 | 0.7542 | 0.9423 | |
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| 0.1054 | 8.0 | 2088 | 0.2731 | 0.7614 | 0.7586 | 0.7600 | 0.9423 | |
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| 0.1054 | 9.0 | 2349 | 0.2763 | 0.7794 | 0.7526 | 0.7658 | 0.9434 | |
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| 0.0771 | 10.0 | 2610 | 0.2829 | 0.7704 | 0.7695 | 0.7700 | 0.9434 | |
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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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