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This is an implementation of the BiDAF model with GloVe embeddings. The basic layout is pretty simple: encode words as a combination of word embeddings and a character-level encoder, pass the word representations through a bi-LSTM/GRU, use a matrix of attentions to put question information into the passage word representations (this is the only part that is at all non-standard), pass this through another few layers of bi-LSTMs/GRUs, and do a softmax over span start and span end.

This model is maintained by the AllenNLP team and its contributors on the AllenNLP models repository at https://github.com/allenai/allennlp-models.

It achieves 76% F1 and 66% exact match on the SQuAD 1.1 validation set.