DrQA

This agent is a (partial) implementation of the DrQA Document Reader from:

Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes. 2017. Reading Wikipedia to Answer Open-Domain Questions. In Association for Computational Linguistics (ACL).

Read more about DrQA here.

Basic Examples

Train a DrQA model on SQuAD.

python projects/drqa/train.py

Evaluate a pre-trained model on SQuAD.

python projects/drqa/eval_pretrained.py

DictionaryAgent Options

BPEHelper Arguments

--bpe-vocab	Path to pre-trained tokenizer vocab
--bpe-merge	Path to pre-trained tokenizer merge

DrqaAgent Options

DrQA Arguments

--no-cuda	Default: False.
--gpu	Default: -1.
--random-seed	Default: 1013.
--embedding-file	File of space separated embeddings: w e1 … ed
--init-model	Load dict/features/weights/opts from this file
--log-file
--fix-embeddings	Default: True.
--tune-partial	Train the K most frequent word embeddings Default: 0.
--embedding-dim	Default embedding size if embedding_file is not given Default: 300.
--hidden-size	Hidden size of RNN units Default: 128.
--doc-layers	Number of RNN layers for passage Default: 3.
--question-layers	Number of RNN layers for question Default: 3.
--rnn-type	RNN type: lstm (default), gru, or rnn Default: lstm.
--valid-metric	Metric for choosing best valid model Choices: accuracy, f1. Default: f1.
--max-len	The max span allowed during decoding Default: 15.
--rnn-padding	Default: False.
--display-iter	Print train error after every<display_iter> epoches (default 10) Default: 10.
--dropout-emb	Dropout rate for word embeddings Default: 0.4.
--dropout-rnn	Dropout rate for RNN states Default: 0.4.
--dropout-rnn-output	Whether to dropout the RNN output Default: True.
--optimizer	Optimizer: sgd or adamax (default) Default: adamax.
--learning-rate, -lr	Learning rate for SGD (default 0.1) Default: 0.1.
--grad-clipping	Gradient clipping (default 10.0) Default: 10.
--weight-decay	Weight decay (default 0) Default: 0.
--momentum	Momentum (default 0) Default: 0.
--subsample-docs	When given paragraphs (separated by ) will take only a subset of them, including the one with the answer for training. Default: 0.
--concat-rnn-layers	Default: True.
--question-merge	The way of computing question representation Default: self_attn.
--use-qemb	Whether to use weighted question embeddings Default: True.
--use-in-question	Whether to use in_question features Default: True.
--use-tf	Whether to use tf features Default: True.
--use-time	Time features marking how recent word was said Default: 0.

BPEHelper Arguments

--bpe-vocab	Path to pre-trained tokenizer vocab
--bpe-merge	Path to pre-trained tokenizer merge
--pretrained-words	Use only words found in provided embedding_file Default: True.

SimpleDictionaryAgent Options

BPEHelper Arguments

--bpe-vocab	Path to pre-trained tokenizer vocab
--bpe-merge	Path to pre-trained tokenizer merge
--pretrained-words	Use only words found in provided embedding_file Default: True.