Long T5 Tglobal Base 16384 Booksum V12

🚀 BigBird - Transformer Model for Long Sequences

This project focuses on BigBird, a Transformer - based model that addresses the computational challenges of applying traditional Transformer models to long sequences. It offers a more efficient way to handle long - form text, such as long document summarization and question - answering with long contexts.

✨ Features

• Efficient for Long Sequences: BigBird uses block sparse attention instead of the normal attention mechanism in BERT, enabling it to handle sequences up to a length of 4096 at a much lower computational cost.
• SOTA Performance: Achieved state - of - the - art results on various tasks involving very long sequences.
• Flexible Application: Can be used for tasks like long document summarization and question - answering.

📦 Installation

No installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

# let's consider following sentence as an example
example = ['BigBird', 'is', 'now', 'available', 'in', 'HuggingFace', 'for', 'extractive', 'question', 'answering']

# further let's assume, we're trying to understand the representation of 'available' i.e.
query_token = 'available'
# We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section.
key_tokens = []

Advanced Usage

# The following code can be used to further analyze the attention mechanism for a given query token
# and build a list of key tokens to attend to.
# This is a more in - depth exploration of how BigBird's attention works.
# The code can be extended to handle more complex scenarios and longer sequences.
# Here we assume we have a function to calculate attention scores and select key tokens.
# For simplicity, we just show the general idea.
# def calculate_attention(query_token, example):
#     # Implement attention calculation logic here
#     pass
# key_tokens = calculate_attention(query_token, example)

📚 Documentation

• Metrics: The model uses ROUGE as a metric for evaluation.
• Datasets: Trained on the kmfoda/booksum dataset.
• Tags: The model is related to tasks such as summarization, long - document processing, and booksum.

Information Table

Widget Examples

The model comes with several widget examples to demonstrate its capabilities:

• Earthquakes: Analyzes the recurrence intervals of large earthquakes along a fault segment.
• Scientific Paper: Discusses a feed - forward neural field algorithm and its applications in solving various problems.
• Transcribed Audio - Lecture: Covers the concept of cross - validation in computational statistics.
• BigBird Blog Intro: Explains the limitations of traditional Transformer models and how BigBird addresses them.
• Rick and Morty Analysis: A humorous analysis of the TV show Rick and Morty, highlighting its intellectual depth.

📄 License

The project is licensed under the following licenses:

• Apache - 2.0
• BSD - 3 - Clause