metadata
language: ko
tags:
- bert
- mrc
datasets:
- klue
license: cc-by-sa-4.0
bert-base for QA
Code: See Ainize Workspace
klue-bert-base-mrc DEMO: Ainize DEMO
klue-bert-base-mrc API: Ainize API
Overview
Language model: klue/bert-base
Language: Korean
Downstream-task: Extractive QA
Training data: KLUE-MRC
Eval data: KLUE-MRC
Usage
In Transformers
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)
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