Dmitry Karpov
2024
DeepPavlov 1.0: Your Gateway to Advanced NLP Models Backed by Transformers and Transfer Learning
Maksim Savkin
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Anastasia Voznyuk
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Fedor Ignatov
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Anna Korzanova
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Dmitry Karpov
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Alexander Popov
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Vasily Konovalov
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
We present DeepPavlov 1.0, an open-source framework for using Natural Language Processing (NLP) models by leveraging transfer learning techniques. DeepPavlov 1.0 is created for modular and configuration-driven development of state-of-the-art NLP models and supports a wide range of NLP model applications. DeepPavlov 1.0 is designed for practitioners with limited knowledge of NLP/ML. DeepPavlov is based on PyTorch and supports HuggingFace transformers. DeepPavlov is publicly released under the Apache 2.0 license and provides access to an online demo.
2023
DeepPavlov Dream: Platform for Building Generative AI Assistants
Diliara Zharikova
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Daniel Kornev
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Fedor Ignatov
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Maxim Talimanchuk
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Dmitry Evseev
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Ksenya Petukhova
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Veronika Smilga
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Dmitry Karpov
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Yana Shishkina
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Dmitry Kosenko
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Mikhail Burtsev
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
An open-source DeepPavlov Dream Platform is specifically tailored for development of complex dialog systems like Generative AI Assistants. The stack prioritizes efficiency, modularity, scalability, and extensibility with the goal to make it easier to develop complex dialog systems from scratch. It supports modular approach to implementation of conversational agents enabling their development through the choice of NLP components and conversational skills from a rich library organized into the distributions of ready-for-use multi-skill AI assistant systems. In DeepPavlov Dream, multi-skill Generative AI Assistant consists of NLP components that extract features from user utterances, conversational skills that generate or retrieve a response, skill and response selectors that facilitate choice of relevant skills and the best response, as well as a conversational orchestrator that enables creation of multi-skill Generative AI Assistants scalable up to industrial grade AI assistants. The platform allows to integrate large language models into dialog pipeline, customize with prompt engineering, handle multiple prompts during the same dialog session and create simple multimodal assistants.