@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",
pages = "465--474",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DeepPavlov 1.0: Your Gateway to Advanced NLP Models Backed by Transformers and Transfer Learning
%A Savkin, Maksim
%A Voznyuk, Anastasia
%A Ignatov, Fedor
%A Korzanova, Anna
%A Karpov, Dmitry
%A Popov, Alexander
%A Konovalov, Vasily
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F savkin-etal-2024-deeppavlov
%X 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.
%U https://aclanthology.org/2024.emnlp-demo.47
%P 465-474
Markdown (Informal)
[DeepPavlov 1.0: Your Gateway to Advanced NLP Models Backed by Transformers and Transfer Learning](https://aclanthology.org/2024.emnlp-demo.47) (Savkin et al., EMNLP 2024)
ACL
- Maksim Savkin, Anastasia Voznyuk, Fedor Ignatov, Anna Korzanova, Dmitry Karpov, Alexander Popov, and Vasily Konovalov. 2024. DeepPavlov 1.0: Your Gateway to Advanced NLP Models Backed by Transformers and Transfer Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 465–474, Miami, Florida, USA. Association for Computational Linguistics.