|
--- |
|
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: "Contoh" |
|
--- |
|
|
|
<!-- 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 |