---
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: 0.4158878504672897
    - name: Recall
      type: recall
      value: 0.5028248587570622
    - name: F1
      type: f1
      value: 0.45524296675191817
    - name: Accuracy
      type: accuracy
      value: 0.8060836501901141
---

<!-- 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: 0.7681
- Precision: 0.4159
- Recall: 0.5028
- F1: 0.4552
- Accuracy: 0.8061

## 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   | 0.9702          | 0.2686    | 0.3672 | 0.3103 | 0.7240   |
| No log        | 2.0   | 50   | 0.8977          | 0.2702    | 0.3785 | 0.3153 | 0.7468   |
| No log        | 3.0   | 75   | 0.8785          | 0.2517    | 0.4124 | 0.3126 | 0.7551   |
| No log        | 4.0   | 100  | 0.8608          | 0.2927    | 0.4746 | 0.3621 | 0.7567   |
| No log        | 5.0   | 125  | 0.7859          | 0.4053    | 0.4350 | 0.4196 | 0.7909   |
| No log        | 6.0   | 150  | 0.7728          | 0.4010    | 0.4350 | 0.4173 | 0.7901   |
| No log        | 7.0   | 175  | 0.7647          | 0.4118    | 0.4746 | 0.4409 | 0.7932   |
| No log        | 8.0   | 200  | 0.7800          | 0.3929    | 0.4972 | 0.4389 | 0.7985   |
| No log        | 9.0   | 225  | 0.7706          | 0.4211    | 0.4972 | 0.4560 | 0.8053   |
| No log        | 10.0  | 250  | 0.7681          | 0.4159    | 0.5028 | 0.4552 | 0.8061   |


### Framework versions

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