---
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: 0.7532580364900087
    - name: Recall
      type: recall
      value: 0.7416595380667237
    - name: F1
      type: f1
      value: 0.7474137931034481
    - name: Accuracy
      type: accuracy
      value: 0.9492845117845118
---

<!-- 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: 0.2432
- Precision: 0.7533
- Recall: 0.7417
- F1: 0.7474
- Accuracy: 0.9493

## 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  | 0.3950          | 0.5892    | 0.4380 | 0.5025 | 0.9104   |
| 0.5722        | 2.0   | 522  | 0.2869          | 0.6306    | 0.6484 | 0.6394 | 0.9311   |
| 0.5722        | 3.0   | 783  | 0.2300          | 0.7047    | 0.6758 | 0.6900 | 0.9452   |
| 0.2424        | 4.0   | 1044 | 0.2293          | 0.6793    | 0.7340 | 0.7056 | 0.9426   |
| 0.2424        | 5.0   | 1305 | 0.2208          | 0.7952    | 0.7074 | 0.7488 | 0.9497   |
| 0.1564        | 6.0   | 1566 | 0.2345          | 0.7104    | 0.7408 | 0.7253 | 0.9447   |
| 0.1564        | 7.0   | 1827 | 0.2312          | 0.6956    | 0.7605 | 0.7266 | 0.9456   |
| 0.112         | 8.0   | 2088 | 0.2404          | 0.7673    | 0.7417 | 0.7542 | 0.9500   |
| 0.112         | 9.0   | 2349 | 0.2303          | 0.7698    | 0.7553 | 0.7625 | 0.9531   |
| 0.0879        | 10.0  | 2610 | 0.2432          | 0.7533    | 0.7417 | 0.7474 | 0.9493   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2