---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
datasets:
- jakartaresearch/indoqa
model-index:
- name: IndoQA
  results: []
language:
- id
pipeline_tag: question-answering
widget:
- text: "Berapa jumlah pulau yang ada di indonesia?"
  context: "Indonesia adalah negara kepulauan, Dengan jumlah pulau sekitar 17 ribu"
  example_title: "Examples"
---

<!-- 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. -->

# IndoQA

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on [jakartaresearch/indoqa](https://huggingface.co/datasets/jakartaresearch/indoqa).
It achieves the following results on the evaluation set:
- Loss: 1.4807

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 207  | 1.9698          |
| No log        | 2.0   | 414  | 1.8862          |
| 0.9416        | 3.0   | 621  | 1.4807          |

### How to use this model in Transformers Library

```python
from transformers import pipeline

question = "Berapa jumlah pulau yang ada di indonesia?"
context = "Indonesia adalah negara kepulauan, Dengan jumlah pulau sekitar 17 ribu"

from transformers import pipeline

question_answerer = pipeline("question-answering", model="digo-prayudha/IndoQA")
question_answerer(question=question, context=context)
```

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0