Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages
Paper • 2112.09866 • Published
How to use bhavikardeshna/xlm-roberta-base-vietnamese with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "question-answering" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# pip install "transformers<5.0.0"
from transformers import pipeline
pipe = pipeline("question-answering", model="bhavikardeshna/xlm-roberta-base-vietnamese") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("bhavikardeshna/xlm-roberta-base-vietnamese")
model = AutoModelForQuestionAnswering.from_pretrained("bhavikardeshna/xlm-roberta-base-vietnamese", device_map="auto")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}