rubert_large_squad_2

Introduction

The rubert_large_squad_2 model is a question-answering model based on the BERT architecture, developed by Den4ikAI. It is implemented using PyTorch and is compatible with Hugging Face's Transformers library. The model is designed to perform inference tasks efficiently and is available under the MIT license.

Architecture

This model utilizes the rubert base model from Sberbank AI, specifically fine-tuned for the SQuAD 2.0 question-answering tasks. It incorporates the BERT transformer architecture, which allows it to understand and process natural language inputs effectively.

Training

The rubert_large_squad_2 model was trained using a dataset with a sample size of 4 and over 4 epochs. It leverages the capabilities of the rubert model for enhanced performance in understanding Russian language texts.

Guide: Running Locally

To run the rubert_large_squad_2 model locally, follow these steps:

1. Install the Transformers library:
   Ensure you have the Hugging Face Transformers library installed. You can install it using pip:

   pip install transformers
   

2. Import and Initialize the Pipeline:
   Use the following code to set up the question-answering pipeline:

   from transformers import pipeline
   
   qa_pipeline = pipeline(
       "question-answering",
       model="Den4ikAI/rubert_large_squad_2",
       tokenizer="Den4ikAI/rubert_large_squad_2"
   )
   

3. Run the Model:
   Provide a context and question to the pipeline to get predictions:

   predictions = qa_pipeline({
       'context': "Пушкин родился 6 июля 1799 года",
       'question': "Когда родился Пушкин?"
   })
   print(predictions)
   

   This will output:

   {'score': 0.8013797664642334, 'start': 15, 'end': 31, 'answer': '6 июля 1799 года'}
   

4. Cloud GPUs:
   For efficient processing, consider using cloud-based GPU services, such as Google Colab, AWS, or Azure, to handle the model's computational requirements.

License

The rubert_large_squad_2 model is released under the MIT license, allowing for widespread use and modification.