---
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: train
      args: lug
    metrics:
    - name: Precision
      type: precision
      value: 0.29015544041450775
    - name: Recall
      type: recall
      value: 0.27722772277227725
    - name: F1
      type: f1
      value: 0.2835443037974684
    - name: Accuracy
      type: accuracy
      value: 0.7297843665768194
---

<!-- 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: 1.0530
- Precision: 0.2902
- Recall: 0.2772
- F1: 0.2835
- Accuracy: 0.7298

## 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   | 25   | 1.2878          | 0.0       | 0.0    | 0.0    | 0.7271   |
| No log        | 2.0   | 50   | 1.2373          | 0.0       | 0.0    | 0.0    | 0.7271   |
| No log        | 3.0   | 75   | 1.2309          | 0.3542    | 0.1683 | 0.2282 | 0.7244   |
| No log        | 4.0   | 100  | 1.1505          | 0.2712    | 0.2376 | 0.2533 | 0.7183   |
| No log        | 5.0   | 125  | 1.1360          | 0.2579    | 0.2426 | 0.25   | 0.7170   |
| No log        | 6.0   | 150  | 1.0932          | 0.3108    | 0.2277 | 0.2629 | 0.7338   |
| No log        | 7.0   | 175  | 1.0761          | 0.2989    | 0.2574 | 0.2766 | 0.7298   |
| No log        | 8.0   | 200  | 1.0645          | 0.2805    | 0.3069 | 0.2931 | 0.7244   |
| No log        | 9.0   | 225  | 1.0577          | 0.3022    | 0.2723 | 0.2865 | 0.7325   |
| No log        | 10.0  | 250  | 1.0530          | 0.2902    | 0.2772 | 0.2835 | 0.7298   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2