Referring Tweets

@GuillaumeLample If you want to train BERT from scratch in @PyTorch, you can check out our XLM repository! Our English model outperforms the original BERT on all GLUE tasks, although it's trained on the same data and without the next sentence prediction task https://t.co/zqFAQd7I1q @alex_conneau
@alex_conneau Using the same data (Wiki/TBC) and without the next sentence prediction task, our GLUE submission outperforms BERT on all tasks. Check out our XLM @PyTorch repo to train BERT from scratch in multiple languages. Code and models available at https://t.co/1An89JVEIh @GuillaumeLample https://t.co/0beXyIRoBr
@ikuyamada FacebookのXLMも、GLUEタスクでBERTを越えたということで、GitHubページを見てみると、BERT-largeの半分しか層はないが(12層)、BERT-largeよりパラメータ数は多く (BERT-largeの約倍)、何故かと思ったらembeddingの次元がBERT-largeの倍の2048次元になっている https://t.co/3InkgYvMOp
@thammegowda I like how https://t.co/jtn1qKyknq is forked out of https://t.co/DwaetzTgSE After decades of Supervised Machine Translation, Unsupervised MT is stealing the stage (BLEU score on En->Fr WMT test) 2017: ~15 2018: ~27 2019 Jan: ~33 2019 May: ~37 35+ BLEU is *really good👏 next?🤔

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