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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] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020.

preprint: https://arxiv.org/abs/2004.03659

@article{10.1093/bioinformatics/btaa675,
    author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
    title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
    journal = {Bioinformatics},
    year = {2020},
    month = {07},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa675},
    url = {https://doi.org/10.1093/bioinformatics/btaa675},
    note = {btaa675},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
} 

[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.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. 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}
}