luganda-ner-v5
This model is a fine-tuned version of masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0 on the lg-ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.2328
- Precision: 0.8503
- Recall: 0.8428
- F1: 0.8465
- Accuracy: 0.9591
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: 6
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 261 | 0.2276 | 0.7703 | 0.6441 | 0.7015 | 0.9353 |
0.3176 | 2.0 | 522 | 0.1848 | 0.8431 | 0.7542 | 0.7962 | 0.9545 |
0.3176 | 3.0 | 783 | 0.1871 | 0.8564 | 0.8173 | 0.8364 | 0.9576 |
0.0753 | 4.0 | 1044 | 0.2015 | 0.8691 | 0.8294 | 0.8488 | 0.9614 |
0.0753 | 5.0 | 1305 | 0.2325 | 0.8616 | 0.8361 | 0.8487 | 0.9584 |
0.0261 | 6.0 | 1566 | 0.2328 | 0.8503 | 0.8428 | 0.8465 | 0.9591 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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Evaluation results
- Precision on lg-nertest set self-reported0.850
- Recall on lg-nertest set self-reported0.843
- F1 on lg-nertest set self-reported0.847
- Accuracy on lg-nertest set self-reported0.959