readme: ---
  language: ko
  tags:
  - bert
  - mrc
  datasets:
  - klue
  license: cc-by-sa-4.0
  ---
  
  # bert-base for QA 
  
  **Code:**  See [Ainize Workspace](https://link.ainize.ai/3FjvBVn) 
  
  **klue-bert-base-mrc DEMO**: [Ainize DEMO](https://main-klue-mrc-bert-scy6500.endpoint.ainize.ai/)
  
  **klue-bert-base-mrc API**: [Ainize API](https://ainize.ai/scy6500/KLUE-MRC-BERT?branch=main)
  
  ## Overview
  **Language model:** klue/bert-base  
  **Language:** Korean  
  **Downstream-task:** Extractive QA    
  **Training data:** KLUE-MRC  
  **Eval data:** KLUE-MRC  
   
  
  
  ## Usage
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("ainize/klue-bert-base-mrc")
  model = AutoModelForQuestionAnswering.from_pretrained("ainize/klue-bert-base-mrc")
  
  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)
  ```
  
  ## About us
  [Teachable NLP](https://ainize.ai/teachable-nlp) - Train NLP models with your own text without writing any code  
  [Ainize](https://ainize.ai/) - Deploy ML project using free gpu
  