SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis
Paper • 1912.09723 • Published • 2
How to use AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru") # Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru")
model = AutoModelForQuestionAnswering.from_pretrained("AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru")Pretrained model using a masked language modeling (MLM) objective. Fine tuned on English and Russian QA datasets
SQuAD + SberQuAD
SberQuAD original paper is here! Recommend to read!
The results obtained are the following (SberQUaD):
f1 = 84.3
exact_match = 65.3