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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: train
args: lug
metrics:
- name: Precision
type: precision
value: 0.4158878504672897
- name: Recall
type: recall
value: 0.5028248587570622
- name: F1
type: f1
value: 0.45524296675191817
- name: Accuracy
type: accuracy
value: 0.8060836501901141
---
<!-- 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: 0.7681
- Precision: 0.4159
- Recall: 0.5028
- F1: 0.4552
- Accuracy: 0.8061
## 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 | 25 | 0.9702 | 0.2686 | 0.3672 | 0.3103 | 0.7240 |
| No log | 2.0 | 50 | 0.8977 | 0.2702 | 0.3785 | 0.3153 | 0.7468 |
| No log | 3.0 | 75 | 0.8785 | 0.2517 | 0.4124 | 0.3126 | 0.7551 |
| No log | 4.0 | 100 | 0.8608 | 0.2927 | 0.4746 | 0.3621 | 0.7567 |
| No log | 5.0 | 125 | 0.7859 | 0.4053 | 0.4350 | 0.4196 | 0.7909 |
| No log | 6.0 | 150 | 0.7728 | 0.4010 | 0.4350 | 0.4173 | 0.7901 |
| No log | 7.0 | 175 | 0.7647 | 0.4118 | 0.4746 | 0.4409 | 0.7932 |
| No log | 8.0 | 200 | 0.7800 | 0.3929 | 0.4972 | 0.4389 | 0.7985 |
| No log | 9.0 | 225 | 0.7706 | 0.4211 | 0.4972 | 0.4560 | 0.8053 |
| No log | 10.0 | 250 | 0.7681 | 0.4159 | 0.5028 | 0.4552 | 0.8061 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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