rubert base cased LLM Model

Introduction

RuBERT is a pre-trained language model specifically adapted for the Russian language. It is based on the BERT architecture and is designed for tasks requiring understanding and generation of Russian text. The model is characterized by having 12 layers, 768 hidden units, 12 attention heads, and approximately 180 million parameters.

Architecture

RuBERT is a Russian, cased model initialized from a multilingual BERT-base model. It was trained on a large corpus consisting of the Russian part of Wikipedia and Russian news data to build a vocabulary of Russian subtokens. The model features:

12 layers
768 hidden units
12 attention heads
180 million parameters

Training

The model was trained using a corpus that includes the Russian Wikipedia and news data. The training process involved adapting the multilingual BERT-base model for Russian by building a comprehensive vocabulary of Russian subtokens. The model was also fine-tuned with Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) tasks to enhance its understanding of the Russian language.

Guide: Running Locally

To run RuBERT locally, follow these steps:

Install Python: Ensure Python is installed on your machine.

Set up a virtual environment:

python -m venv rubert-env
source rubert-env/bin/activate  # On Windows use `rubert-env\Scripts\activate`

Install Hugging Face Transformers library:
```
pip install transformers
```

Load the RuBERT model:

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/rubert-base-cased")
model = AutoModel.from_pretrained("DeepPavlov/rubert-base-cased")

Inference: Use the tokenizer and model for feature extraction or other NLP tasks.

For improved performance, consider using cloud GPUs from platforms like AWS, Google Cloud, or Azure.

License

The RuBERT model is available under licensing terms specified on the Hugging Face model card. Users should refer to the model's license information for detailed terms and conditions.

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