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---
license: mit
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v3
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: 0.8141289437585734
- name: Recall
type: recall
value: 0.7971793149764943
- name: F1
type: f1
value: 0.8055649813369528
- name: Accuracy
type: accuracy
value: 0.952700740525628
---
<!-- 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-v3
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2295
- Precision: 0.8141
- Recall: 0.7972
- F1: 0.8056
- Accuracy: 0.9527
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 261 | 0.4226 | 0.6273 | 0.3606 | 0.4580 | 0.8928 |
| 0.5572 | 2.0 | 522 | 0.2835 | 0.7720 | 0.6185 | 0.6868 | 0.9219 |
| 0.5572 | 3.0 | 783 | 0.2740 | 0.7579 | 0.7401 | 0.7489 | 0.9311 |
| 0.1745 | 4.0 | 1044 | 0.2423 | 0.7895 | 0.7683 | 0.7788 | 0.9399 |
| 0.1745 | 5.0 | 1305 | 0.2273 | 0.8048 | 0.7945 | 0.7996 | 0.9498 |
| 0.086 | 6.0 | 1566 | 0.2295 | 0.8141 | 0.7972 | 0.8056 | 0.9527 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|