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# luganda-ner-v6

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: 0.2811
- Precision: 0.8030
- Recall: 0.7992
- F1: 0.8011
- Accuracy: 0.9467

## 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  | 0.5150          | 0.4947    | 0.2841 | 0.3609 | 0.8692   |
| 0.6193        | 2.0   | 522  | 0.3422          | 0.7491    | 0.5393 | 0.6271 | 0.9161   |
| 0.6193        | 3.0   | 783  | 0.2737          | 0.7744    | 0.6595 | 0.7124 | 0.9306   |
| 0.2505        | 4.0   | 1044 | 0.3201          | 0.7343    | 0.7072 | 0.7205 | 0.9141   |
| 0.2505        | 5.0   | 1305 | 0.2564          | 0.7887    | 0.7569 | 0.7724 | 0.9375   |
| 0.1474        | 6.0   | 1566 | 0.2461          | 0.8173    | 0.7569 | 0.7859 | 0.9459   |
| 0.1474        | 7.0   | 1827 | 0.2739          | 0.8004    | 0.7757 | 0.7879 | 0.9434   |
| 0.0956        | 8.0   | 2088 | 0.2566          | 0.8100    | 0.7905 | 0.8001 | 0.9486   |
| 0.0956        | 9.0   | 2349 | 0.2709          | 0.7859    | 0.7938 | 0.7898 | 0.9463   |
| 0.0712        | 10.0  | 2610 | 0.2811          | 0.8030    | 0.7992 | 0.8011 | 0.9467   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["lg-ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "luganda-ner-v6", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "lg-ner", "type": "lg-ner", "config": "lug", "split": "test", "args": "lug"}, "metrics": [{"type": "precision", "value": 0.8029689608636977, "name": "Precision"}, {"type": "recall", "value": 0.7991940899932841, "name": "Recall"}, {"type": "f1", "value": 0.8010770784247729, "name": "F1"}, {"type": "accuracy", "value": 0.9467474952809641, "name": "Accuracy"}]}]}]}
transformers pytorch safetensors xlm-roberta token-classification generated_from_trainer dataset:lg-ner base_model:xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-lg-ner #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

luganda-ner-v6
==============


This model is a fine-tuned version of xlm-roberta-base on the lg-ner dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2811
* Precision: 0.8030
* Recall: 0.7992
* F1: 0.8011
* Accuracy: 0.9467


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



### Framework versions


* Transformers 4.34.1
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1



### 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 ### Framework versions


* Transformers 4.34.1
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-lg-ner #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.34.1
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-lg-ner #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.34.1
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1