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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: 0.9352766798418972
- name: Recall
type: recall
value: 0.9288518155053974
- name: F1
type: f1
value: 0.93205317577548
- name: Accuracy
type: accuracy
value: 0.9817219554779573
---
<!-- 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: 0.0955
- Precision: 0.9353
- Recall: 0.9289
- F1: 0.9321
- Accuracy: 0.9817
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5913 | 1.0 | 609 | 0.2667 | 0.6740 | 0.7620 | 0.7153 | 0.9336 |
| 0.2461 | 2.0 | 1218 | 0.1704 | 0.7981 | 0.8437 | 0.8203 | 0.9562 |
| 0.1784 | 3.0 | 1827 | 0.1273 | 0.8578 | 0.8943 | 0.8757 | 0.9669 |
| 0.1337 | 4.0 | 2436 | 0.1048 | 0.8731 | 0.9132 | 0.8927 | 0.9726 |
| 0.0868 | 5.0 | 3045 | 0.0988 | 0.9129 | 0.9178 | 0.9153 | 0.9760 |
| 0.0736 | 6.0 | 3654 | 0.0961 | 0.9146 | 0.9225 | 0.9185 | 0.9781 |
| 0.0602 | 7.0 | 4263 | 0.0877 | 0.9270 | 0.9222 | 0.9246 | 0.9798 |
| 0.0566 | 8.0 | 4872 | 0.0948 | 0.9281 | 0.9222 | 0.9252 | 0.9807 |
| 0.0514 | 9.0 | 5481 | 0.0930 | 0.9349 | 0.9271 | 0.9310 | 0.9817 |
| 0.0395 | 10.0 | 6090 | 0.0955 | 0.9353 | 0.9289 | 0.9321 | 0.9817 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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