
<!-- 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-v5

This model is a fine-tuned version of [masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0](https://huggingface.co/masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2328
- Precision: 0.8503
- Recall: 0.8428
- F1: 0.8465
- Accuracy: 0.9591

## 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  | 0.2276          | 0.7703    | 0.6441 | 0.7015 | 0.9353   |
| 0.3176        | 2.0   | 522  | 0.1848          | 0.8431    | 0.7542 | 0.7962 | 0.9545   |
| 0.3176        | 3.0   | 783  | 0.1871          | 0.8564    | 0.8173 | 0.8364 | 0.9576   |
| 0.0753        | 4.0   | 1044 | 0.2015          | 0.8691    | 0.8294 | 0.8488 | 0.9614   |
| 0.0753        | 5.0   | 1305 | 0.2325          | 0.8616    | 0.8361 | 0.8487 | 0.9584   |
| 0.0261        | 6.0   | 1566 | 0.2328          | 0.8503    | 0.8428 | 0.8465 | 0.9591   |


### Framework versions

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

{"license": "afl-3.0", "tags": ["generated_from_trainer"], "datasets": ["lg-ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "luganda-ner-v5", "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": 0.8502710027100271, "name": "Precision"}, {"type": "recall", "value": 0.8428475486903962, "name": "Recall"}, {"type": "f1", "value": 0.8465430016863407, "name": "F1"}, {"type": "accuracy", "value": 0.959089589080877, "name": "Accuracy"}]}]}]}
transformers pytorch xlm-roberta token-classification generated_from_trainer dataset:lg-ner license:afl-3.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-lg-ner #license-afl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

luganda-ner-v5
==============


This model is a fine-tuned version of masakhane/afroxlmr-large-ner-masakhaner-1.0\_2.0 on the lg-ner dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2328
* Precision: 0.8503
* Recall: 0.8428
* F1: 0.8465
* Accuracy: 0.9591


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-afl-3.0 #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-afl-3.0 #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