readme_template.md

---
language:
- id
license: cc-by-4.0
library_name: nemo
datasets:
- SQuAD-indonesian-language-(442-Context-Title)
thumbnail: null
tags:
- natural-language-processing
- qna
- chatbot
- bert
- Transformer
- NeMo
- pytorch
model-index:
- name: nlp_id_qa_bert_base_uncased
  results: []

---


## Model Overview

This model was built using the NeMo Nvidia BERTQAModel, using the SQuAD v2.0 dataset in Indonesian.

## NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
``` 

## How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

### Automatically instantiate the model

```python
import nemo.collections.nlp as model
model = nemo_nlp.models.question_answering.qa_bert_model.BERTQAModel.from_pretrained("raihanpf22/nlp_id_qa_bert_base_uncased")
```

### Transcribing using Python

Simply do:
```
eval_device = [config.trainer.devices[0]] if isinstance(config.trainer.devices, list) else 1
model.trainer = pl.Trainer(
    devices=eval_device,
    accelerator=config.trainer.accelerator,
    precision=16,
    logger=False,
)

config.exp_manager.create_checkpoint_callback = False
exp_dir = exp_manager(model.trainer, config.exp_manager)
output_nbest_file = os.path.join(exp_dir, "output_nbest_file.json")
output_prediction_file = os.path.join(exp_dir, "output_prediction_file.json")

all_preds, all_nbest = model.inference(
    "questions.json",
    output_prediction_file=output_prediction_file,
    output_nbest_file=output_nbest_file,
    num_samples=-1, # setting to -1 will use all samples for inference
)

for question_id in all_preds:
    print(all_preds[question_id])
```

### Input

This model accepts SQuAD Format v2.0 as input.

### Output

This model provides output in the form of answers to questions according to the existing context.

## Model Architecture

Using an uncased BERT base architecture model.

## Training

50 Epochs, 8 Batch size per GPU, 1 num_layer

### Datasets

using SQuAD v2.0 as train data

## Performance

test_HasAns_exact = 98.0
test_HasAns_f1 = 98.0465087890625
test_HasAns_total = 100.0
test_NoAns_exact = 0.0
test_NoAns_f1 = 0.0
test_NoAns_total = 0.0
test_exact = 98.0
test_f1 = 98.0465087890625
test_loss = 0.00019806227646768093
test_total = 100.0

## References

<ADD ANY REFERENCES HERE AS NEEDED>

[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)