bigbird pegasus large pubmed

Introduction

The BigBird-Pegasus model, specifically the bigbird-pegasus-large-pubmed variant, is a transformer model designed for handling long sequences efficiently. It employs sparse attention mechanisms, allowing it to process sequences up to a length of 4096 tokens at reduced computational cost, compared to traditional models like BERT. This model is particularly effective for tasks such as summarizing lengthy documents and question-answering with extensive contexts.

Architecture

BigBird uses block sparse attention instead of the standard attention mechanisms found in models like BERT. This allows it to handle longer sequences more efficiently. The model architecture includes the ability to use different attention types, with the default being block sparse attention for the encoder. The decoder, however, uses full original attention.

Training

The bigbird-pegasus-large-pubmed model is fine-tuned on the PubMed dataset, part of the scientific papers collection. This fine-tuning focuses on enhancing the model's summarization capabilities for scientific documents.

Guide: Running Locally

To use the model locally with PyTorch, follow these steps:

1. Install Transformers: Ensure you have the transformers library installed.

   pip install transformers
   

2. Load the Model and Tokenizer: Use the code snippet below to load the model and tokenizer.

   from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-pubmed")
   model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed")
   

3. Generate Summaries: Prepare your text for summarization.

   text = "Replace me by any text you'd like."
   inputs = tokenizer(text, return_tensors='pt')
   prediction = model.generate(**inputs)
   summary = tokenizer.batch_decode(prediction)
   

4. Optional Configuration: You can adjust block_size and num_random_blocks for different attention configurations.

For enhanced performance, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The BigBird-Pegasus model is released under the Apache 2.0 license, permitting use, distribution, and modification under the terms specified in the license.