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DEPRECATED

DeepQA is built on top of Keras. We've decided that pytorch is a better platform for NLP research. We re-wrote DeepQA into a pytorch library called AllenNLP. There will be no more development of DeepQA. But, we're pretty excited about AllenNLP - if you're doing deep learning for natural language processing, you should check it out!

DeepQA

DeepQA is a library for doing high-level NLP tasks with deep learning, particularly focused on various kinds of question answering. DeepQA is built on top of Keras and TensorFlow, and can be thought of as an interface to these systems that makes NLP easier.

Specifically, this library provides the following benefits over plain Keras / TensorFlow:

Running DeepQA

Setting up a development environment

DeepQA is built using Python 3. The easiest way to set up a compatible environment is to use Conda. This will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run DeepQA.

  1. Download and install Conda.

  2. Create a Conda environment with Python 3.

    conda create -n deep_qa python=3.5
  3. Now activate the Conda environment.

    source activate deep_qa
  4. Install the required dependencies.

    ./scripts/install_requirements.sh
  5. Set the PYTHONHASHSEED for repeatable experiments.

    export PYTHONHASHSEED=2157

You should now be able to test your installation with pytest -v. Congratulations! You now have a development environment for deep_qa that uses TensorFlow with CPU support. (For GPU support, see requirements.txt for information on how to install tensorflow-gpu).

Using DeepQA as an executable

To train or evaluate a model using a clone of the DeepQA repository, the recommended entry point is to use the run_model.py script. The first argument to that script is a parameter file, described more below. The second argument determines the behavior, either training a model or evaluating a trained model against a test dataset. Current valid options for the second argument are train and test (omitting the argument is the same as passing train).

Parameter files specify the model class you're using, model hyperparameters, training details, data files, data generator details, and many other things. You can see example parameter files in the examples directory. You can get some notion of what parameters are available by looking through the documentation.

Actually training a model will require input files, which you need to provide. We have a companion library, DeepQA Experiments, which was originally designed to produce input files and run experiments, and can be used to generate required data files for most of the tasks we have models for. We're moving towards putting the data processing code directly into DeepQA, so that DeepQA Experiments is not necessary, but for now, getting training data files in the right format is most easily done with DeepQA Experiments.

Using DeepQA as a library

If you are using DeepQA as a library in your own code, it is still straightforward to run your model. Instead of using the run_model.py script to do the training/evaluation, you can do it yourself as follows:

from deep_qa import run_model, evaluate_model, load_model, score_dataset

# Train a model given a json specification
run_model("/path/to/json/parameter/file")

# Load a model given a json specification
loaded_model = load_model("/path/to/json/parameter/file")
# Do some more exciting things with your model here!

# Get predictions from a pre-trained model on some test data specified in the json parameters.
predictions = score_dataset("/path/to/json/parameter/file")
# Compute your own metrics, or do beam search, or whatever you want with the predictions here.

# Compute Keras' metrics on a test dataset, using a pre-trained model.
evaluate_model("/path/to/json/parameter/file", ["/path/to/data/file"])

The rest of the usage guidelines, examples, etc., are the same as when working in a clone of the repository.

Implementing your own models

To implement a new model in DeepQA, you need to subclass TextTrainer. There is documentation on what is necessary for this; see in particular the Abstract methods section. For a simple example of a fully functional model, see the simple sequence tagger, which has about 20 lines of actual implementation code.

In order to train, load and evaluate models which you have written yourself, simply pass an additional argument to the functions above and remove the model_class parameter from your json specification. For example:

from deep_qa import run_model
from .local_project import MyGreatModel

# Train a model given a json specification (without a "model_class" attribute).
run_model("/path/to/json/parameter/file", model_class=MyGreatModel)

If you're doing a new task, or a new variant of a task with a different input/output specification, you probably also need to implement an Instance type. The Instance handles reading data from a file and converting it into numpy arrays that can be used for training and evaluation. This only needs to happen once for each input/output spec.

Implemented models

DeepQA has implementations of state-of-the-art methods for a variety of tasks. Here are a few of them:

Reading comprehension

Entailment

Datasets

This code allows for easy experimentation with the following datasets:

Note that the data processing code for most of this currently lives in DeepQA Experiments, however.

Contributing

If you use this code and think something could be improved, pull requests are very welcome. Opening an issue is ok, too, but we can respond much more quickly to pull requests.

Contributors

License

This code is released under the terms of the Apache 2 license.