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DeepPavlov/rubert-base-cased-conversational

Model

24

stars

12

commits

10

repos using this model

2

linked in READMEs

Nov 8, 2021

updated

bert
endpoints_compatible
feature-extraction
jax
pytorch
transformers
Browse cluster: Multilingual BERT Models →

README

rubert-base-cased-conversational

Conversational RuBERT (Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2]. We assembled a new vocabulary for Conversational RuBERT model on this data and initialized the model with RuBERT.

08.11.2021: upload model with MLM and NSP heads

[1]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)

[2]: Shavrina T., Shapovalova O. (2017) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.

Contributors

system

8 commits

patrickvonplaten

2 commits

julien-c

1 commits

YK
Yura Kuratov

1 commits

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DeepPavlov/rubert-base-cased-conversational

Model

24

stars

12

commits

10

repos using this model

2

linked in READMEs

Nov 8, 2021

updated

bert
endpoints_compatible
feature-extraction
jax
pytorch
transformers
Browse cluster: Multilingual BERT Models →

README

rubert-base-cased-conversational

Conversational RuBERT (Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2]. We assembled a new vocabulary for Conversational RuBERT model on this data and initialized the model with RuBERT.

08.11.2021: upload model with MLM and NSP heads

[1]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)

[2]: Shavrina T., Shapovalova O. (2017) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.

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Contributors

system

8 commits

patrickvonplaten

2 commits

julien-c

1 commits

YK
Yura Kuratov

1 commits