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# 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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["lg-ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "luganda-ner-v4", "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": 0.7540871934604905, "name": "Precision"}, {"type": "recall", "value": 0.7454545454545455, "name": "Recall"}, {"type": "f1", "value": 0.7497460209955976, "name": "F1"}, {"type": "accuracy", "value": 0.9360226606759132, "name": "Accuracy"}]}]}]}
transformers pytorch deberta-v2 token-classification generated_from_trainer dataset:lg-ner license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #token-classification #generated_from_trainer #dataset-lg-ner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

luganda-ner-v4
==============


This model is a fine-tuned version of 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



### 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 #deberta-v2 #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 #deberta-v2 #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