---
license: mit
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v3
  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: 0.7824620573355818
    - name: Recall
      type: recall
      value: 0.7938408896492729
    - name: F1
      type: f1
      value: 0.7881104033970276
    - name: Accuracy
      type: accuracy
      value: 0.9543876262626263
---

<!-- 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-v3

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2106
- Precision: 0.7825
- Recall: 0.7938
- F1: 0.7881
- Accuracy: 0.9544

## 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 261  | 0.3026          | 0.7257    | 0.5047 | 0.5954 | 0.9270   |
| 0.4863        | 2.0   | 522  | 0.2362          | 0.7214    | 0.6801 | 0.7001 | 0.9426   |
| 0.4863        | 3.0   | 783  | 0.2053          | 0.7226    | 0.7622 | 0.7419 | 0.9474   |
| 0.1814        | 4.0   | 1044 | 0.2115          | 0.7081    | 0.7802 | 0.7424 | 0.9465   |
| 0.1814        | 5.0   | 1305 | 0.1974          | 0.7850    | 0.7964 | 0.7907 | 0.9568   |
| 0.1011        | 6.0   | 1566 | 0.2106          | 0.7825    | 0.7938 | 0.7881 | 0.9544   |


### Framework versions

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