Instructions to use DeepPavlov/rubert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepPavlov/rubert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeepPavlov/rubert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/rubert-base-cased") model = AutoModel.from_pretrained("DeepPavlov/rubert-base-cased") - Inference
- Notebooks
- Google Colab
- Kaggle
| language: | |
| - ru | |
| # rubert-base-cased | |
| RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\]. | |
| 08.11.2021: upload model with MLM and NSP heads | |
| \[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213). | |