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https://api-inference.huggingface.co/models/DeepPavlov/rubert-base-cased-conversational
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DeepPavlov/rubert-base-cased-conversational DeepPavlov/rubert-base-cased-conversational
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pytorch

tf

Contributed by

DeepPavlov DeepPavlov MIPT university
6 models

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

			
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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/rubert-base-cased-conversational") model = AutoModelWithLMHead.from_pretrained("DeepPavlov/rubert-base-cased-conversational")

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.

[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.