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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v6
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: 0.8029689608636977
- name: Recall
type: recall
value: 0.7991940899932841
- name: F1
type: f1
value: 0.8010770784247729
- name: Accuracy
type: accuracy
value: 0.9467474952809641
---
<!-- 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-v6
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2811
- Precision: 0.8030
- Recall: 0.7992
- F1: 0.8011
- Accuracy: 0.9467
## 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 | 0.5150 | 0.4947 | 0.2841 | 0.3609 | 0.8692 |
| 0.6193 | 2.0 | 522 | 0.3422 | 0.7491 | 0.5393 | 0.6271 | 0.9161 |
| 0.6193 | 3.0 | 783 | 0.2737 | 0.7744 | 0.6595 | 0.7124 | 0.9306 |
| 0.2505 | 4.0 | 1044 | 0.3201 | 0.7343 | 0.7072 | 0.7205 | 0.9141 |
| 0.2505 | 5.0 | 1305 | 0.2564 | 0.7887 | 0.7569 | 0.7724 | 0.9375 |
| 0.1474 | 6.0 | 1566 | 0.2461 | 0.8173 | 0.7569 | 0.7859 | 0.9459 |
| 0.1474 | 7.0 | 1827 | 0.2739 | 0.8004 | 0.7757 | 0.7879 | 0.9434 |
| 0.0956 | 8.0 | 2088 | 0.2566 | 0.8100 | 0.7905 | 0.8001 | 0.9486 |
| 0.0956 | 9.0 | 2349 | 0.2709 | 0.7859 | 0.7938 | 0.7898 | 0.9463 |
| 0.0712 | 10.0 | 2610 | 0.2811 | 0.8030 | 0.7992 | 0.8011 | 0.9467 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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