Create README.md

README.md

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+#### 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
+#### Language: Korean
+#### Downstream-task: Extractive QA
+#### Training data: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
+#### Eval data: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
+#### Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/EastHShin/Klue-CommonSense-workspace)
+
+### 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)
+```