huggingface/transformers: ELECTRA, Bad word filters, bugfixes & improvements

ELECTRA Model (@LysandreJik)

ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN. At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the SQuAD 2.0 dataset.

This release comes with 6 ELECTRA checkpoints:

• google/electra-small-discriminator
• google/electra-small-generator
• google/electra-base-discriminator
• google/electra-base-generator
• google/electra-large-discriminator
• google/electra-large-generator

Related:

• Paper
• Official code
• Models available in the community models
• Docs

Thanks to the author @clarkkev for his help during the implementation.

The generate method now has a bad word filter.

• Decoder input ids are not necessary for T5 training anymore (@patrickvonplaten)
• Update encoder and decoder on set_input_embedding for BART (@sshleifer)
• Using loaded checkpoint with --do_predict (instead of random init) for Pytorch-lightning scripts (@ethanjperez)
• Clean summarization and translation example testing files for T5 and Bart (@patrickvonplaten)
• Cleaner examples (@julien-c)
• Extensive testing for T5 model (@patrickvonplaten)
• Force models outputs to always have batch_size as their first dim (@patrickvonplaten)
• Fix for continuing training in some scripts (@xeb)
• Resizing embedding matrix before sending it to the optimizer (@ngarneau)
• BertJapaneseTokenizer accept options for mecab (@tamuhey)
• Speed up GELU computation with torch.jit (@mryab)
• fix argument order of update_mems fn in TF version (@patrickvonplaten, @dmytyar)
• Split generate test function into beam search, no beam search (@patrickvonplaten)