---
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