rajcscw/nlp-gym

NLPGym - A toolkit to develop RL agents to solve NLP tasks.

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Emerging

This toolkit helps machine learning researchers develop and benchmark reinforcement learning (RL) agents for natural language processing (NLP) tasks. It provides simulated environments for common NLP problems like sequence tagging (e.g., named entity recognition), multiple-choice question answering, and multi-label text classification. Researchers can feed in text data and observe how RL agents learn to make sequential decisions to process language, ultimately evaluating their agent's performance.

202 stars. No commits in the last 6 months. Available on PyPI.

Use this if you are a machine learning researcher exploring how reinforcement learning techniques can be applied to solve complex natural language processing tasks.

Not ideal if you are a practitioner looking for an off-the-shelf NLP solution or a library to build standard NLP models without focusing on reinforcement learning experimentation.

natural-language-processing reinforcement-learning-research text-classification question-answering sequence-tagging
Stale 6m
Maintenance 0 / 25
Adoption 10 / 25
Maturity 25 / 25
Community 14 / 25

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Stars

202

Forks

22

Language

Python

License

MIT

Last pushed

Apr 19, 2022

Commits (30d)

0

Dependencies

15

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