---
license: afl-3.0
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v5
  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: 0.8502710027100271
    - name: Recall
      type: recall
      value: 0.8428475486903962
    - name: F1
      type: f1
      value: 0.8465430016863407
    - name: Accuracy
      type: accuracy
      value: 0.959089589080877
---

<!-- 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-v5

This model is a fine-tuned version of [masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0](https://huggingface.co/masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2328
- Precision: 0.8503
- Recall: 0.8428
- F1: 0.8465
- Accuracy: 0.9591

## 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  | 0.2276          | 0.7703    | 0.6441 | 0.7015 | 0.9353   |
| 0.3176        | 2.0   | 522  | 0.1848          | 0.8431    | 0.7542 | 0.7962 | 0.9545   |
| 0.3176        | 3.0   | 783  | 0.1871          | 0.8564    | 0.8173 | 0.8364 | 0.9576   |
| 0.0753        | 4.0   | 1044 | 0.2015          | 0.8691    | 0.8294 | 0.8488 | 0.9614   |
| 0.0753        | 5.0   | 1305 | 0.2325          | 0.8616    | 0.8361 | 0.8487 | 0.9584   |
| 0.0261        | 6.0   | 1566 | 0.2328          | 0.8503    | 0.8428 | 0.8465 | 0.9591   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2