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# luganda-ner-v3

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2295
- Precision: 0.8141
- Recall: 0.7972
- F1: 0.8056
- Accuracy: 0.9527

## 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 261  | 0.4226          | 0.6273    | 0.3606 | 0.4580 | 0.8928   |
| 0.5572        | 2.0   | 522  | 0.2835          | 0.7720    | 0.6185 | 0.6868 | 0.9219   |
| 0.5572        | 3.0   | 783  | 0.2740          | 0.7579    | 0.7401 | 0.7489 | 0.9311   |
| 0.1745        | 4.0   | 1044 | 0.2423          | 0.7895    | 0.7683 | 0.7788 | 0.9399   |
| 0.1745        | 5.0   | 1305 | 0.2273          | 0.8048    | 0.7945 | 0.7996 | 0.9498   |
| 0.086         | 6.0   | 1566 | 0.2295          | 0.8141    | 0.7972 | 0.8056 | 0.9527   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["lg-ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "luganda-ner-v3", "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": 0.8141289437585734, "name": "Precision"}, {"type": "recall", "value": 0.7971793149764943, "name": "Recall"}, {"type": "f1", "value": 0.8055649813369528, "name": "F1"}, {"type": "accuracy", "value": 0.952700740525628, "name": "Accuracy"}]}]}]}
transformers pytorch xlm-roberta token-classification generated_from_trainer dataset:lg-ner license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-lg-ner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

luganda-ner-v3
==============


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


* Loss: 0.2295
* Precision: 0.8141
* Recall: 0.7972
* F1: 0.8056
* Accuracy: 0.9527


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: 6


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### 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: 6 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-lg-ner #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: 6 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-lg-ner #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: 6### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2