Instructions to use ai-forever/ruRoberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai-forever/ruRoberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ai-forever/ruRoberta-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ai-forever/ruRoberta-large") model = AutoModelForMaskedLM.from_pretrained("ai-forever/ruRoberta-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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Download README.md from ai-forever/ruRoberta-large: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
-
https://huggingface.co/ai-forever/ruRoberta-large/resolve/main/README.md
- Command line
-
hf download hf://ai-forever/ruRoberta-large/README.md
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curl -L -o README.md https://huggingface.co/ai-forever/ruRoberta-large/resolve/main/README.md
1.14 kB
metadata
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: https://github.com/sberbank-ai/model-zoo
ruRoberta-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model is pretrained by the SberDevices team.
- Task:
mask filling - Type:
encoder - Tokenizer:
BBPE - Dict size:
50 257 - Num Parameters:
355 M - Training Data Volume
250 GB
Authors
- NLP core team RnD Telegram channel:
- Dmitry Zmitrovich
Cite us
@misc{zmitrovich2023family,
title={A Family of Pretrained Transformer Language Models for Russian},
author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova},
year={2023},
eprint={2309.10931},
archivePrefix={arXiv},
primaryClass={cs.CL}
}