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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: 0.79182156133829
- name: Recall
type: recall
value: 0.7842415316642121
- name: F1
type: f1
value: 0.788013318534961
- name: Accuracy
type: accuracy
value: 0.9559346774929295
---
<!-- 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: 0.3199
- Precision: 0.7918
- Recall: 0.7842
- F1: 0.7880
- Accuracy: 0.9559
## 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 | 0.2380 | 0.7942 | 0.7106 | 0.7501 | 0.9526 |
| 0.0954 | 2.0 | 522 | 0.2345 | 0.7954 | 0.7872 | 0.7913 | 0.9558 |
| 0.0954 | 3.0 | 783 | 0.2560 | 0.8168 | 0.7518 | 0.7830 | 0.9555 |
| 0.0562 | 4.0 | 1044 | 0.2815 | 0.7261 | 0.7791 | 0.7517 | 0.9477 |
| 0.0562 | 5.0 | 1305 | 0.2738 | 0.7744 | 0.8012 | 0.7875 | 0.9566 |
| 0.0345 | 6.0 | 1566 | 0.2951 | 0.8083 | 0.7732 | 0.7904 | 0.9556 |
| 0.0345 | 7.0 | 1827 | 0.3026 | 0.7741 | 0.7872 | 0.7806 | 0.9547 |
| 0.0215 | 8.0 | 2088 | 0.3062 | 0.8159 | 0.7636 | 0.7889 | 0.9563 |
| 0.0215 | 9.0 | 2349 | 0.3157 | 0.7959 | 0.7813 | 0.7886 | 0.9563 |
| 0.017 | 10.0 | 2610 | 0.3199 | 0.7918 | 0.7842 | 0.7880 | 0.9559 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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