Upload config

readme.md

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+---
+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)
+