---
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: 0.7958904109589041
    - name: Recall
      type: recall
      value: 0.7803895231699127
    - name: F1
      type: f1
      value: 0.7880637504238724
    - name: Accuracy
      type: accuracy
      value: 0.9450776825903877
---

<!-- 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: 0.2863
- Precision: 0.7959
- Recall: 0.7804
- F1: 0.7881
- Accuracy: 0.9451

## 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  | 0.5454          | 0.4946    | 0.2471 | 0.3296 | 0.8597   |
| 0.6168        | 2.0   | 522  | 0.3598          | 0.6993    | 0.5608 | 0.6224 | 0.9078   |
| 0.6168        | 3.0   | 783  | 0.3842          | 0.6326    | 0.6696 | 0.6506 | 0.8941   |
| 0.2599        | 4.0   | 1044 | 0.2755          | 0.7857    | 0.6870 | 0.7331 | 0.9333   |
| 0.2599        | 5.0   | 1305 | 0.2642          | 0.8019    | 0.7206 | 0.7591 | 0.9351   |
| 0.1498        | 6.0   | 1566 | 0.2755          | 0.7886    | 0.7589 | 0.7734 | 0.9385   |
| 0.1498        | 7.0   | 1827 | 0.2601          | 0.7945    | 0.7609 | 0.7774 | 0.9458   |
| 0.1023        | 8.0   | 2088 | 0.2889          | 0.7875    | 0.7717 | 0.7795 | 0.9409   |
| 0.1023        | 9.0   | 2349 | 0.2819          | 0.8082    | 0.7670 | 0.7870 | 0.9460   |
| 0.0716        | 10.0  | 2610 | 0.2863          | 0.7959    | 0.7804 | 0.7881 | 0.9451   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2