readme: 
  #### Klue-bert base for Common Sense QA
  
  #### Klue-CommonSense-model DEMO: [Ainize DEMO](https://main-klue-common-sense-qa-east-h-shin.endpoint.ainize.ai/)
  
  #### Klue-CommonSense-model API: [Ainize API](https://ainize.ai/EastHShin/Klue-CommonSense_QA?branch=main)
  
  ### Overview
  
  **Language model**: klue/bert-base
  <br>
  **Language**: Korean
  <br>
  **Downstream-task**: Extractive QA
  <br>
  **Training data**: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
  <br>
  **Eval data**: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
  <br>
  **Code**: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/EastHShin/Klue-CommonSense-workspace)
  <br>
  
  ### Usage
  
  ### In Transformers
  ```
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("EasthShin/Klue-CommonSense-model")
  model = AutoModelForQuestionAnswering.from_pretrained("EasthShin/Klue-CommonSense-model")
  
  context = "your context"
  question = "your question"
  
  encodings = tokenizer(context, question, max_length=512, truncation=True,
                        padding="max_length", return_token_type_ids=False)
  encodings = {key: torch.tensor([val]) for key, val in encodings.items()}             
  
  input_ids = encodings["input_ids"]
  attention_mask = encodings["attention_mask"]
  
  pred = model(input_ids, attention_mask=attention_mask)
  
  start_logits, end_logits = pred.start_logits, pred.end_logits
  
  token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1)
  
  pred_ids = input_ids[0][token_start_index: token_end_index + 1]
  
  prediction = tokenizer.decode(pred_ids)
  ```