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---
license: mit
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v1
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: 0.7987967914438503
- name: Recall
type: recall
value: 0.8025520483546004
- name: F1
type: f1
value: 0.8006700167504188
- name: Accuracy
type: accuracy
value: 0.9451502831421519
---
<!-- 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-v1
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: 0.3188
- Precision: 0.7988
- Recall: 0.8026
- F1: 0.8007
- Accuracy: 0.9452
## 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 | 0.2915 | 0.7456 | 0.6891 | 0.7162 | 0.9240 |
| 0.2284 | 2.0 | 522 | 0.2965 | 0.7393 | 0.7314 | 0.7353 | 0.9294 |
| 0.2284 | 3.0 | 783 | 0.2830 | 0.7426 | 0.7576 | 0.7500 | 0.9271 |
| 0.1426 | 4.0 | 1044 | 0.2710 | 0.7935 | 0.7690 | 0.7810 | 0.9387 |
| 0.1426 | 5.0 | 1305 | 0.2805 | 0.8087 | 0.7636 | 0.7855 | 0.9389 |
| 0.0881 | 6.0 | 1566 | 0.2992 | 0.7734 | 0.7884 | 0.7808 | 0.9404 |
| 0.0881 | 7.0 | 1827 | 0.2746 | 0.8109 | 0.7864 | 0.7985 | 0.9457 |
| 0.0582 | 8.0 | 2088 | 0.3149 | 0.7753 | 0.7925 | 0.7838 | 0.9400 |
| 0.0582 | 9.0 | 2349 | 0.3179 | 0.7940 | 0.7945 | 0.7942 | 0.9440 |
| 0.0403 | 10.0 | 2610 | 0.3188 | 0.7988 | 0.8026 | 0.8007 | 0.9452 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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