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---
license: mit
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v2
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: 0.7704421562689279
- name: Recall
type: recall
value: 0.7695099818511797
- name: F1
type: f1
value: 0.7699757869249395
- name: Accuracy
type: accuracy
value: 0.9434371807967313
---
<!-- 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-v2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2829
- Precision: 0.7704
- Recall: 0.7695
- F1: 0.7700
- Accuracy: 0.9434
## 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 | 0.4835 | 0.5191 | 0.3037 | 0.3832 | 0.8719 |
| 0.5738 | 2.0 | 522 | 0.3454 | 0.7288 | 0.5203 | 0.6071 | 0.9117 |
| 0.5738 | 3.0 | 783 | 0.2956 | 0.7752 | 0.6612 | 0.7137 | 0.9235 |
| 0.2549 | 4.0 | 1044 | 0.2791 | 0.7537 | 0.6848 | 0.7176 | 0.9258 |
| 0.2549 | 5.0 | 1305 | 0.2801 | 0.7530 | 0.7211 | 0.7367 | 0.9335 |
| 0.1566 | 6.0 | 1566 | 0.2675 | 0.7956 | 0.7229 | 0.7575 | 0.9393 |
| 0.1566 | 7.0 | 1827 | 0.2610 | 0.7744 | 0.7350 | 0.7542 | 0.9423 |
| 0.1054 | 8.0 | 2088 | 0.2731 | 0.7614 | 0.7586 | 0.7600 | 0.9423 |
| 0.1054 | 9.0 | 2349 | 0.2763 | 0.7794 | 0.7526 | 0.7658 | 0.9434 |
| 0.0771 | 10.0 | 2610 | 0.2829 | 0.7704 | 0.7695 | 0.7700 | 0.9434 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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