klue-bert-base-mrc / README.md
---
language: ko
tags:
- bert
- mrc
datasets:
- klue
license: cc-by-nc-4.0
---
## Usage
```python
# Load Transformers library
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
context = "your context"
question = "your question"
# Load fine-tuned MRC model by HuggingFace Model Hub
HUGGINGFACE_MODEL_PATH = "bespin-global/klue-bert-base-mrc"
tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_PATH )
model = AutoModelForQuestionAnswering.from_pretrained(HUGGINGFACE_MODEL_PATH )
# Encoding
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"]
# Predict
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]
# Decoding
prediction = tokenizer.decode(pred_ids)
```
## Citing & Authors
<!--- Describe where people can find more information -->
[Jaehyeong](https://huggingface.co/jaehyeong) at [Bespin Global](https://www.bespinglobal.com/)