Instructions to use DeepPavlov/bert-base-bg-cs-pl-ru-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepPavlov/bert-base-bg-cs-pl-ru-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeepPavlov/bert-base-bg-cs-pl-ru-cased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/bert-base-bg-cs-pl-ru-cased") model = AutoModel.from_pretrained("DeepPavlov/bert-base-bg-cs-pl-ru-cased") - Notebooks
- Google Colab
- Kaggle
bert-base-bg-cs-pl-ru-cased
SlavicBERT[1] (Slavic (bg, cs, pl, ru), cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) was trained on Russian News and four Wikipedias: Bulgarian, Czech, Polish, and Russian. Subtoken vocabulary was built using this data. Multilingual BERT was used as an initialization for SlavicBERT.
08.11.2021: upload model with MLM and NSP heads
[1]: Arkhipov M., Trofimova M., Kuratov Y., Sorokin A. (2019). Tuning Multilingual Transformers for Language-Specific Named Entity Recognition. ACL anthology W19-3712.
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