add readme

README.md

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+---
+datasets:
+- KLUE-MRC
+license: cc-by-sa-4.0
+---
+
+# bert-base for QA 
+NOTE: You can try the model through the [Ainize DEMO](https://main-klue-mrc-bert-scy6500.endpoint.ainize.ai/), and you can call the api through the [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  
+**Code:**  See [Ainize Workspace]()  
+
+
+## Usage
+### In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("./mrc-bert-base")
+model = AutoModelForQuestionAnswering.from_pretrained("./mrc-bert-base")
+
+context = "your context"
+question = "your question"
+
+encodings = tokenizer(context, question, max_length=512, truncation=True,
+                      padding="max_length", return_token_type_ids=False)
+
+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