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# small-yoruba-finetuned-ner

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5450
- Precision: 0.7748
- Recall: 0.7748
- F1: 0.7890
- Accuracy: 0.8967

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 13   | 1.1087          | 0.5043    | 0.5043 | 0.5225 | 0.6713   |
| No log        | 2.0   | 26   | 1.0303          | 0.4       | 0.4    | 0.4229 | 0.6297   |
| No log        | 3.0   | 39   | 0.7622          | 0.6147    | 0.6147 | 0.6204 | 0.7456   |
| No log        | 4.0   | 52   | 0.8148          | 0.5688    | 0.5688 | 0.5741 | 0.7103   |
| No log        | 5.0   | 65   | 0.6816          | 0.6053    | 0.6053 | 0.6244 | 0.7834   |
| No log        | 6.0   | 78   | 0.7372          | 0.5826    | 0.5826 | 0.6036 | 0.8048   |
| No log        | 7.0   | 91   | 0.5917          | 0.7593    | 0.7593 | 0.7628 | 0.8866   |
| No log        | 8.0   | 104  | 0.5758          | 0.7155    | 0.7155 | 0.7444 | 0.8829   |
| No log        | 9.0   | 117  | 0.5806          | 0.6903    | 0.6903 | 0.7091 | 0.8741   |
| No log        | 10.0  | 130  | 0.5254          | 0.7522    | 0.7522 | 0.7727 | 0.9005   |
| No log        | 11.0  | 143  | 0.5422          | 0.7636    | 0.7636 | 0.7742 | 0.8942   |
| No log        | 12.0  | 156  | 0.5469          | 0.75      | 0.75   | 0.7671 | 0.8879   |
| No log        | 13.0  | 169  | 0.5410          | 0.7890    | 0.7890 | 0.7963 | 0.8942   |
| No log        | 14.0  | 182  | 0.5435          | 0.7890    | 0.7890 | 0.7963 | 0.8942   |
| No log        | 15.0  | 195  | 0.5450          | 0.7748    | 0.7748 | 0.7890 | 0.8967   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "small-yoruba-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "config": "yo", "split": "validation", "args": "yo"}, "metrics": [{"type": "precision", "value": 0.7747747747747747, "name": "Precision"}, {"type": "recall", "value": 0.7747747747747747, "name": "Recall"}, {"type": "f1", "value": 0.7889908256880734, "name": "F1"}, {"type": "accuracy", "value": 0.8967254408060453, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:wikiann license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

small-yoruba-finetuned-ner
==========================


This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5450
* Precision: 0.7748
* Recall: 0.7748
* F1: 0.7890
* Accuracy: 0.8967


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: 15


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #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: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3