ParlAI
ParlAI is an open-source software platform for developing conversational AI agents. It provides an interface to interact with different dialogue datasets, evaluate models, train new models from scratch, and integrate new datasets.
opensource
dialogue
datasets
models
training
agents
ParlAI: Open-Source Conversational AI Platform
An open-source software platform for developing conversational AI agents, with features to interact with dialogue datasets, evaluate models, train new models and integrate new datasets.
What is ParlAI?
ParlAI (pronounced "par-lay") is an open-source software platform created by Facebook AI Research for developing conversational artificial intelligence agents. Its key features include:
- Unified framework for interacting with many popular dialogue datasets like SQuAD, bAbI tasks, MOOC, Twitter, etc.
- Support for training and evaluating chit-chat models, question answering, task completion and more
- Tools for crowdsourcing the collection of new datasets
- Integration with Amazon Mechanical Turk for data collection and human evaluation
- Reference implementations of state-of-the-art algorithms like memory networks, attentive LSTMs, neural belief trackers etc.
- Built-in standard measurements and popular datasets for fair evaluation and comparison of different models
- Seamless integration with PyTorch and Lua Torch for neural network development
The goal of ParlAI is to push forward conversational AI research by enabling fast prototyping and testing of new models on a diverse range of datasets. It reduces entry barriers for new researchers and unifies existing work into one platform for easy comparison.
ParlAI Features
Features
- Provides a unified framework for training and evaluating AI models on a variety of datasets
- Supports multi-turn dialog with context
- Includes popular datasets like SQuAD, bAbI tasks, Wizard of Wikipedia, Empathetic Dialogues
- Allows seamless integration of new datasets
- Provides integration with Amazon Mechanical Turk for data collection
- Supports training models like memory networks, seq2seq, transformers etc
- Has built-in implementations of popular models like BERT, GPT-2
Pricing
- Open Source
Pros
Unified framework reduces effort to train/evaluate on new datasets
Pretrained models allow quick prototyping
Active development community
Well documented
Cons
Less flexibility compared to building custom models from scratch
Pretrained models can be resource intensive
Some documentation aspects could be improved
Official Links
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