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cimm-kzn/rudr-bert cimm-kzn/rudr-bert
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pytorch

tf

Contributed by

Chemoinformatics and Molecular Modeling Laboratory KFU university
1 team member · 3 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cimm-kzn/rudr-bert") model = AutoModel.from_pretrained("cimm-kzn/rudr-bert")

RuDR-BERT

RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper.
link: https://yadi.sk/d/-PTn0xhk1PqvgQ

Citing & Authors

If you find this repository helpful, feel free to cite our publication:

[1] https://arxiv.org/abs/2004.03659

@misc{tutubalina2020russian,
    title={The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews},
    author={Elena Tutubalina and Ilseyar Alimova and Zulfat Miftahutdinov and Andrey Sakhovskiy and Valentin Malykh and Sergey Nikolenko},
    year={2020},
    eprint={2004.03659},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects. link to paper

@article{tutubalina2017using,
    title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
    author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
    journal={Russian Chemical Bulletin},
    volume={66},
    number={11},
    pages={2180--2189},
    year={2017},
    publisher={Springer}
}