mT5: A massively multilingual pre-trained text-to-text transformer

10/22/2020
by   Linting Xue, et al.
0

The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints used in this work are publicly available.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/03/2020

Testing pre-trained Transformer models for Lithuanian news clustering

A recent introduction of Transformer deep learning architecture made bre...
research
07/11/2023

PIGEON: Predicting Image Geolocations

We introduce PIGEON, a multi-task end-to-end system for planet-scale ima...
research
06/11/2022

An Evaluation of OCR on Egocentric Data

In this paper, we evaluate state-of-the-art OCR methods on Egocentric da...
research
02/02/2023

idT5: Indonesian Version of Multilingual T5 Transformer

Indonesian language is spoken by almost 200 million people and is the 10...
research
05/18/2023

mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences

We present our work on developing a multilingual, efficient text-to-text...
research
11/12/2022

AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities

In this work, we present a conceptually simple and effective method to t...
research
07/05/2023

Multilingual Controllable Transformer-Based Lexical Simplification

Text is by far the most ubiquitous source of knowledge and information a...

Please sign up or login with your details

Forgot password? Click here to reset