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--- |
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language: ko |
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tags: |
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- bert |
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- mrc |
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datasets: |
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- klue |
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license: cc-by-sa-4.0 |
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--- |
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# bert-base for QA |
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**Code:** See [Ainize Workspace](https://link.ainize.ai/3FjvBVn) |
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**klue-bert-base-mrc DEMO**: [Ainize DEMO](https://main-klue-mrc-bert-scy6500.endpoint.ainize.ai/) |
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**klue-bert-base-mrc API**: [Ainize API](https://ainize.ai/scy6500/KLUE-MRC-BERT?branch=main) |
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## Overview |
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**Language model:** klue/bert-base |
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**Language:** Korean |
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**Downstream-task:** Extractive QA |
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**Training data:** KLUE-MRC |
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**Eval data:** KLUE-MRC |
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## Usage |
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### In Transformers |
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("ainize/klue-bert-base-mrc") |
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model = AutoModelForQuestionAnswering.from_pretrained("ainize/klue-bert-base-mrc") |
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context = "your context" |
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question = "your question" |
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encodings = tokenizer(context, question, max_length=512, truncation=True, |
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padding="max_length", return_token_type_ids=False) |
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encodings = {key: torch.tensor([val]) for key, val in encodings.items()} |
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input_ids = encodings["input_ids"] |
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attention_mask = encodings["attention_mask"] |
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pred = model(input_ids, attention_mask=attention_mask) |
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start_logits, end_logits = pred.start_logits, pred.end_logits |
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token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1) |
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pred_ids = input_ids[0][token_start_index: token_end_index + 1] |
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prediction = tokenizer.decode(pred_ids) |
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``` |
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## About us |
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[Teachable NLP](https://ainize.ai/teachable-nlp) - Train NLP models with your own text without writing any code |
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[Ainize](https://ainize.ai/) - Deploy ML project using free gpu |
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