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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: 0.7540871934604905
- name: Recall
type: recall
value: 0.7454545454545455
- name: F1
type: f1
value: 0.7497460209955976
- name: Accuracy
type: accuracy
value: 0.9360226606759132
---
<!-- 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: 0.3024
- Precision: 0.7541
- Recall: 0.7455
- F1: 0.7497
- Accuracy: 0.9360
## 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 | 0.4811 | 0.5366 | 0.2768 | 0.3652 | 0.8752 |
| 0.5133 | 2.0 | 522 | 0.3632 | 0.6560 | 0.5380 | 0.5912 | 0.9021 |
| 0.5133 | 3.0 | 783 | 0.3104 | 0.7069 | 0.5993 | 0.6487 | 0.9207 |
| 0.2592 | 4.0 | 1044 | 0.3339 | 0.7494 | 0.6303 | 0.6847 | 0.9269 |
| 0.2592 | 5.0 | 1305 | 0.3153 | 0.7513 | 0.6593 | 0.7023 | 0.9318 |
| 0.167 | 6.0 | 1566 | 0.3071 | 0.7190 | 0.7219 | 0.7204 | 0.9291 |
| 0.167 | 7.0 | 1827 | 0.3072 | 0.7955 | 0.7071 | 0.7487 | 0.9360 |
| 0.1191 | 8.0 | 2088 | 0.3133 | 0.7505 | 0.7455 | 0.7480 | 0.9339 |
| 0.1191 | 9.0 | 2349 | 0.3132 | 0.7510 | 0.7394 | 0.7452 | 0.9349 |
| 0.092 | 10.0 | 2610 | 0.3024 | 0.7541 | 0.7455 | 0.7497 | 0.9360 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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