Question answering on russian with XLMRobertaLarge as a service

Gladkikh Prohor

Last update: Jul 4, 2022

Related tags

Text Processing qa-roberta-ru-saas

Overview

QA Roberta Ru SaaS

Question answering on russian with XLMRobertaLarge as a service. Thanks for the model to Alexander Kaigorodov.

Stack

Flask
Gunicorn

Build image

sudo docker build . --tag qa-roberta-ru-saas

Run on CPU and predict

sudo docker run --rm -p 8080:8080 --name qa-roberta-ru-saas qa-roberta-ru-saas

curl -H "Content-Type: application/json" --data @tests/app/data/test_input.json 0.0.0.0:8080/predict

Run on GPU

Change device to cuda:0 in config before docker build:

device: cuda:0

After build:

sudo docker run --rm --gpus 0 -p 8080:8080 --name qa-roberta-ru-saas qa-roberta-ru-saas

To run with restart:

sudo docker run --gpus 0 -p 8080:8080 --restart always --name qa-roberta-ru-saas qa-roberta-ru-saas

To stop it later:

docker update --restart unless-stopped qa-roberta-ru-saas

To run tests:

pytest tests/

To run app without docker container

PYTHONPATH=. python app/app_main.py

TODO:

GPU/CPU support
Support of context longer than 512 bpe
Predict on long context with sliding window

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Question answering on russian with XLMRobertaLarge as a service

Related tags

Overview

QA Roberta Ru SaaS

Stack

Build image

Run on CPU and predict

Run on GPU

To run tests:

To run app without docker container

TODO:

You might also like...

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GrailQA: Strongly Generalizable Question Answering

Binary Passage Retriever (BPR) - an efficient passage retriever for open-domain question answering

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NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (CVPR2021)

FeTaQA: Free-form Table Question Answering

Baseline code for Korean open domain question answering(ODQA)

improvement of CLIP features over the traditional resnet features on the visual question answering, image captioning, navigation and visual entailment tasks.

Pytorch implementation for the EMNLP 2020 (Findings) paper: Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering

This is the official implementation of "One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval".

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