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# luganda-ner-v2

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2829
- Precision: 0.7704
- Recall: 0.7695
- F1: 0.7700
- Accuracy: 0.9434

## 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  | 0.4835          | 0.5191    | 0.3037 | 0.3832 | 0.8719   |
| 0.5738        | 2.0   | 522  | 0.3454          | 0.7288    | 0.5203 | 0.6071 | 0.9117   |
| 0.5738        | 3.0   | 783  | 0.2956          | 0.7752    | 0.6612 | 0.7137 | 0.9235   |
| 0.2549        | 4.0   | 1044 | 0.2791          | 0.7537    | 0.6848 | 0.7176 | 0.9258   |
| 0.2549        | 5.0   | 1305 | 0.2801          | 0.7530    | 0.7211 | 0.7367 | 0.9335   |
| 0.1566        | 6.0   | 1566 | 0.2675          | 0.7956    | 0.7229 | 0.7575 | 0.9393   |
| 0.1566        | 7.0   | 1827 | 0.2610          | 0.7744    | 0.7350 | 0.7542 | 0.9423   |
| 0.1054        | 8.0   | 2088 | 0.2731          | 0.7614    | 0.7586 | 0.7600 | 0.9423   |
| 0.1054        | 9.0   | 2349 | 0.2763          | 0.7794    | 0.7526 | 0.7658 | 0.9434   |
| 0.0771        | 10.0  | 2610 | 0.2829          | 0.7704    | 0.7695 | 0.7700 | 0.9434   |


### 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-v2", "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": 0.7704421562689279, "name": "Precision"}, {"type": "recall", "value": 0.7695099818511797, "name": "Recall"}, {"type": "f1", "value": 0.7699757869249395, "name": "F1"}, {"type": "accuracy", "value": 0.9434371807967313, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification generated_from_trainer dataset:lg-ner license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-lg-ner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

luganda-ner-v2
==============


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


* Loss: 0.2829
* Precision: 0.7704
* Recall: 0.7695
* F1: 0.7700
* Accuracy: 0.9434


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 4.27.4
* 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: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 10### Training results### Framework versions


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