EMNLP 2024system demonstrations1 citations
DeepPavlov 1.0: Your Gateway to Advanced NLP Models Backed by Transformers and Transfer Learning
Maksim Savkin, Anastasia Voznyuk, Fedor Ignatov, Anna Korzanova, Dmitry Karpov, Alexander Popov, Vasily Konovalov
Abstract
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.
BibTeX
@inproceedings{savkin-etal-2024-deeppavlov,
title = "{D}eep{P}avlov 1.0: Your Gateway to Advanced {NLP} Models Backed by Transformers and Transfer Learning",
author = "Savkin, Maksim and
Voznyuk, Anastasia and
Ignatov, Fedor and
Korzanova, Anna and
Karpov, Dmitry and
Popov, Alexander and
Konovalov, Vasily",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.47/",
doi = "10.18653/v1/2024.emnlp-demo.47",
pages = "465--474"
}