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---
license: mit
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: 0.8241451500348919
- name: Recall
type: recall
value: 0.7931497649429147
- name: F1
type: f1
value: 0.8083504449007528
- name: Accuracy
type: accuracy
value: 0.9525918396979817
---
<!-- 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 [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2417
- Precision: 0.8241
- Recall: 0.7931
- F1: 0.8084
- Accuracy: 0.9526
## 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 | 0.4290 | 0.5281 | 0.3096 | 0.3903 | 0.8864 |
| 0.5483 | 2.0 | 522 | 0.2873 | 0.7307 | 0.5776 | 0.6452 | 0.9216 |
| 0.5483 | 3.0 | 783 | 0.2482 | 0.7745 | 0.6783 | 0.7232 | 0.9334 |
| 0.1931 | 4.0 | 1044 | 0.2472 | 0.7671 | 0.6991 | 0.7316 | 0.9360 |
| 0.1931 | 5.0 | 1305 | 0.2425 | 0.8053 | 0.7388 | 0.7706 | 0.9433 |
| 0.1016 | 6.0 | 1566 | 0.2157 | 0.8253 | 0.7710 | 0.7972 | 0.9490 |
| 0.1016 | 7.0 | 1827 | 0.2332 | 0.8161 | 0.7717 | 0.7932 | 0.9501 |
| 0.0654 | 8.0 | 2088 | 0.2375 | 0.8312 | 0.7804 | 0.8050 | 0.9514 |
| 0.0654 | 9.0 | 2349 | 0.2367 | 0.8309 | 0.7884 | 0.8091 | 0.9528 |
| 0.047 | 10.0 | 2610 | 0.2417 | 0.8241 | 0.7931 | 0.8084 | 0.9526 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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