Klue Bert Base Mrc

🚀 bert-base for QA

This project provides a BERT-based model for Korean question answering tasks, using KLUE-MRC data for training and evaluation.

🚀 Quick Start

• Code: Check out the code on Ainize Workspace.
• klue-bert-base-mrc DEMO: Try the live demo at Ainize DEMO.
• klue-bert-base-mrc API: Access the API at Ainize API.

✨ Features

• Language model: klue/bert-base
• Language: Korean
• Downstream-task: Extractive QA
• Training data: KLUE-MRC
• Eval data: KLUE-MRC

💻 Usage Examples

Basic Usage

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)

📄 License

This project is licensed under the CC BY-SA 4.0 license.

📚 About Us

• Teachable NLP - Train NLP models with your own text without writing any code.
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