Language modeling is the task of predicting the next word or character in a document.
( Image credit: Exploring the Limits of Language Modeling )
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We propose a new benchmark corpus to be used for measuring progress in statistical language modeling.
#14 best model for Language Modelling on One Billion Word
We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference.
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.
#5 best model for Semantic Textual Similarity on MRPC
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
On unsupervised machine translation, we obtain 34. 3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU.
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
SOTA for Question Answering on SQuAD2.0 dev (using extra training data)
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
SOTA for Language Modelling on Text8 (using extra training data)