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Intelligent Conversation Engine: Code and Pre-trained Systems
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requirements.txt Icecaps 0.1.0 Aug 29, 2019


Microsoft Icecaps is an open-source toolkit for building neural conversational systems. Icecaps provides an array of tools from recent conversation modeling and general NLP literature within a flexible paradigm that enables complex multi-task learning setups.

Icecaps is currently on version 0.1.3. We have a number of features in the works for version 0.2, including new models and new data processing functionality. Please be aware that Icecaps 0.2 will not guarantee backwards compatibility and may cause breaking changes.


Icecaps is intended for Python environments and is built on top of TensorFlow. We recommend using Icecaps in an Anaconda environment with Python 3.7. Once you have created an environment, run the following command to install all required dependencies:

pip install -r requirements.txt

If your machine has a GPU, we recommend you instead install from requirements-gpu.txt.


We have provided some scripts in the examples/ directory. These scripts will introduce you to Icecaps' architecture, and we encourage you to use them as templates.

examples/ is our "Hello World" script: it builds a simple seq2seq training scenario while demonstrating the basic five-phase pattern that Icecaps scripts follow.

examples/ presents a more complex system that introduces component chaining and multi-task learning, the core aspects of Icecaps' architecture.

Finally, examples/ gives an example of how to convert a raw text dataset to TFRecord files, which Icecaps uses to feed its data pipelines during training.

We plan to publish more tutorials on other kinds of conversational scenarios in the future.

Pre-Trained Systems

We plan to add pre-trained systems based on cutting-edge conversational modeling literature to Icecaps in the future. We had hoped to include these systems with Icecaps at launch. However, given that these systems may produce toxic responses in some contexts, we have decided to explore improved content-filtering techniques before releasing these models to the public. Follow this repository to stay up to date with new pre-trained system releases.


Visit our homepage here:

View our system demonstration paper from ACL 2019 here:


This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact with any additional questions or comments.

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