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---
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: test
args: lug
metrics:
- name: Precision
type: precision
value: 0.7532580364900087
- name: Recall
type: recall
value: 0.7416595380667237
- name: F1
type: f1
value: 0.7474137931034481
- name: Accuracy
type: accuracy
value: 0.9492845117845118
---
<!-- 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: 0.2432
- Precision: 0.7533
- Recall: 0.7417
- F1: 0.7474
- Accuracy: 0.9493
## 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 | 0.3950 | 0.5892 | 0.4380 | 0.5025 | 0.9104 |
| 0.5722 | 2.0 | 522 | 0.2869 | 0.6306 | 0.6484 | 0.6394 | 0.9311 |
| 0.5722 | 3.0 | 783 | 0.2300 | 0.7047 | 0.6758 | 0.6900 | 0.9452 |
| 0.2424 | 4.0 | 1044 | 0.2293 | 0.6793 | 0.7340 | 0.7056 | 0.9426 |
| 0.2424 | 5.0 | 1305 | 0.2208 | 0.7952 | 0.7074 | 0.7488 | 0.9497 |
| 0.1564 | 6.0 | 1566 | 0.2345 | 0.7104 | 0.7408 | 0.7253 | 0.9447 |
| 0.1564 | 7.0 | 1827 | 0.2312 | 0.6956 | 0.7605 | 0.7266 | 0.9456 |
| 0.112 | 8.0 | 2088 | 0.2404 | 0.7673 | 0.7417 | 0.7542 | 0.9500 |
| 0.112 | 9.0 | 2349 | 0.2303 | 0.7698 | 0.7553 | 0.7625 | 0.9531 |
| 0.0879 | 10.0 | 2610 | 0.2432 | 0.7533 | 0.7417 | 0.7474 | 0.9493 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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