---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- hausa_voa_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-hausa_ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: hausa_voa_ner
      type: hausa_voa_ner
      config: hausa_voa_ner
      split: validation
      args: hausa_voa_ner
    metrics:
    - name: Precision
      type: precision
      value: 0.6781609195402298
    - name: Recall
      type: recall
      value: 0.7763157894736842
    - name: F1
      type: f1
      value: 0.7239263803680982
    - name: Accuracy
      type: accuracy
      value: 0.9516353514265832
---

<!-- 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. -->

# bert-finetuned-hausa_ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the hausa_voa_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1734
- Precision: 0.6782
- Recall: 0.7763
- F1: 0.7239
- Accuracy: 0.9516

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 127  | 0.2162          | 0.6992    | 0.7342 | 0.7163 | 0.9516   |
| No log        | 2.0   | 254  | 0.1702          | 0.6900    | 0.7789 | 0.7318 | 0.9518   |
| No log        | 3.0   | 381  | 0.1734          | 0.6782    | 0.7763 | 0.7239 | 0.9516   |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3