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---
license: mit
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v4
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: 0.7849185946872322
- name: Recall
type: recall
value: 0.7862660944206008
- name: F1
type: f1
value: 0.7855917667238421
- name: Accuracy
type: accuracy
value: 0.9542220362038296
---
<!-- 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-v4
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2222
- Precision: 0.7849
- Recall: 0.7863
- F1: 0.7856
- Accuracy: 0.9542
## 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 | 0.3533 | 0.6141 | 0.4644 | 0.5288 | 0.9208 |
| 0.5126 | 2.0 | 522 | 0.2765 | 0.6658 | 0.6567 | 0.6612 | 0.9326 |
| 0.5126 | 3.0 | 783 | 0.2336 | 0.6834 | 0.7133 | 0.6980 | 0.9433 |
| 0.2374 | 4.0 | 1044 | 0.2207 | 0.7358 | 0.7433 | 0.7395 | 0.9489 |
| 0.2374 | 5.0 | 1305 | 0.2134 | 0.7796 | 0.7528 | 0.7659 | 0.9525 |
| 0.1646 | 6.0 | 1566 | 0.2359 | 0.7423 | 0.7665 | 0.7542 | 0.9484 |
| 0.1646 | 7.0 | 1827 | 0.2223 | 0.7807 | 0.7854 | 0.7831 | 0.9541 |
| 0.1219 | 8.0 | 2088 | 0.2300 | 0.8140 | 0.7665 | 0.7896 | 0.9557 |
| 0.1219 | 9.0 | 2349 | 0.2223 | 0.7733 | 0.7966 | 0.7848 | 0.9547 |
| 0.1016 | 10.0 | 2610 | 0.2222 | 0.7849 | 0.7863 | 0.7856 | 0.9542 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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