---
license: mit
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v2
  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: 0.79182156133829
    - name: Recall
      type: recall
      value: 0.7842415316642121
    - name: F1
      type: f1
      value: 0.788013318534961
    - name: Accuracy
      type: accuracy
      value: 0.9559346774929295
---

<!-- 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-v2

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3199
- Precision: 0.7918
- Recall: 0.7842
- F1: 0.7880
- Accuracy: 0.9559

## 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  | 0.2380          | 0.7942    | 0.7106 | 0.7501 | 0.9526   |
| 0.0954        | 2.0   | 522  | 0.2345          | 0.7954    | 0.7872 | 0.7913 | 0.9558   |
| 0.0954        | 3.0   | 783  | 0.2560          | 0.8168    | 0.7518 | 0.7830 | 0.9555   |
| 0.0562        | 4.0   | 1044 | 0.2815          | 0.7261    | 0.7791 | 0.7517 | 0.9477   |
| 0.0562        | 5.0   | 1305 | 0.2738          | 0.7744    | 0.8012 | 0.7875 | 0.9566   |
| 0.0345        | 6.0   | 1566 | 0.2951          | 0.8083    | 0.7732 | 0.7904 | 0.9556   |
| 0.0345        | 7.0   | 1827 | 0.3026          | 0.7741    | 0.7872 | 0.7806 | 0.9547   |
| 0.0215        | 8.0   | 2088 | 0.3062          | 0.8159    | 0.7636 | 0.7889 | 0.9563   |
| 0.0215        | 9.0   | 2349 | 0.3157          | 0.7959    | 0.7813 | 0.7886 | 0.9563   |
| 0.017         | 10.0  | 2610 | 0.3199          | 0.7918    | 0.7842 | 0.7880 | 0.9559   |


### Framework versions

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