| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - lg-ner |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: luganda-ner-v4 |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: lg-ner |
| type: lg-ner |
| config: lug |
| split: test |
| args: lug |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.7540871934604905 |
| - name: Recall |
| type: recall |
| value: 0.7454545454545455 |
| - name: F1 |
| type: f1 |
| value: 0.7497460209955976 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9360226606759132 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # luganda-ner-v4 |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the lg-ner dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3024 |
| - Precision: 0.7541 |
| - Recall: 0.7455 |
| - F1: 0.7497 |
| - Accuracy: 0.9360 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 261 | 0.4811 | 0.5366 | 0.2768 | 0.3652 | 0.8752 | |
| | 0.5133 | 2.0 | 522 | 0.3632 | 0.6560 | 0.5380 | 0.5912 | 0.9021 | |
| | 0.5133 | 3.0 | 783 | 0.3104 | 0.7069 | 0.5993 | 0.6487 | 0.9207 | |
| | 0.2592 | 4.0 | 1044 | 0.3339 | 0.7494 | 0.6303 | 0.6847 | 0.9269 | |
| | 0.2592 | 5.0 | 1305 | 0.3153 | 0.7513 | 0.6593 | 0.7023 | 0.9318 | |
| | 0.167 | 6.0 | 1566 | 0.3071 | 0.7190 | 0.7219 | 0.7204 | 0.9291 | |
| | 0.167 | 7.0 | 1827 | 0.3072 | 0.7955 | 0.7071 | 0.7487 | 0.9360 | |
| | 0.1191 | 8.0 | 2088 | 0.3133 | 0.7505 | 0.7455 | 0.7480 | 0.9339 | |
| | 0.1191 | 9.0 | 2349 | 0.3132 | 0.7510 | 0.7394 | 0.7452 | 0.9349 | |
| | 0.092 | 10.0 | 2610 | 0.3024 | 0.7541 | 0.7455 | 0.7497 | 0.9360 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.27.4 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.2 |
|
|
