A unified platform for sharing, training and evaluating dialog models across many tasks.

Many popular datasets available all in one place -- with the ability to multi-task over them.

Supports dialog models in PyTorch, Tensorflow and other frameworks.

Seamless integration of Amazon Mechanical Turk for data collection, training and human evaluation.

See Examples Fork me on GitHub

What's New

2018-01-23: Several new tasks added: SNLI, MultiNLI, COPA, NarrativeQA, Twitter and Persona-Chat.

2017-12-14: Fast, multiprocessed data loading supported with Pytorch data loader

2017-11-30: Several new tasks added: SCAN, ConvAI, NVLR and ISWLT14.

2017-10-19: ParlAI Request For Proposals: Winners Announced!

2017-10-13: New model added: Fairseq-py

2017-10-12: New task added: Stanford's MutualFriends

2017-09-22: New task added: babi+

2017-09-21: New task added: WMT En-De training set, with more WMT tasks on the way

2017-08-25: New task added: Deal or No Deal

2017-08-15: New task added: CLEVR

2017-07-20: ParlAI Request For Proposals: Funding university teams - 7 awards are available - deadline Aug 25

2017-07-20: added building an (seq2seq) agent tutorial

2017-07-12: Several new tasks added: MS Marco, TriviaQA, InsuranceQA, personalized-dialog and MNIST_QA

2017-06-27: ExecutableWorld class for interactive worlds with dialog

2017-06-21: MTurk now supports multiple assignments per HIT

2017-06-20: updated MTurk tutorial to reflect new design

2017-06-20: MTurk now uses general world and agent classes

2017-06-16: added Creating a New Task tutorial

2017-05-31: added Seq2Seq model

2017-05-30: added interactive mode with local human agent

2017-05-22: added MTurk tutorial

2017-05-14: added basic tutorial

2017-05-15: ParlAI press: TechCrunch, CNBC, The Verge, Scientific American, Engadget, Venture Beat, Wired, MIT Technology review.

2017-05-12: added VQA V2.0 and Visual Dialog V0.9 tasks

2017-05-01: ParlAI released!

Get Started

Check out our GitHub repository:

Run this command:
git clone
cd ParlAI; python develop


Display 10 random examples from task 1 of the "1k training examples" bAbI task:

Run this command:
python examples/ -t babi:task1k:1

Displays 100 random examples from multitasking on the bAbI task and the SQuAD dataset at the same time:

Run this command:
python examples/ -t babi:task1k:1,squad -n 100

Evaluate an IR baseline model on the validation set of the Movies Subreddit dataset:

Run this command:
python examples/ -m ir_baseline -t "#moviedd-reddit" -dt valid

Display the predictions of that same IR baseline model:

Run this command:
python examples/ -m ir_baseline -t "#moviedd-reddit" -dt valid

Train a simple cpu-based memory network on the "10k training examples" bAbI task 1 with 8 threads (python processes) using Hogwild (requires zmq and Lua Torch):

Run this command:
python examples/memnn_luatorch_cpu/ -t babi:task10k:1 -nt 8

Trains an attentive LSTM model on the SQuAD dataset with a batch size of 32 examples (pytorch and regex):

Run this command:
python examples/drqa/ -t squad -bs 32

For more examples, please read our tutorial. To learn more about ParlAI, click here.