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Bigbird

Bigbird is an open-source repository documenting a port of Google’s BigBird transformer to Hugging Face Transformers, with PyTorch and Flax/JAX model variants, training scripts, and notebooks for evaluation and inference.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bigbird — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need example notebooks or training scripts for exploring BigBird in Hugging Face Transformers, especially for Natural Questions question answering and long-sequence evaluation.

Who it is for

  • Researchers exploring long-sequence transformer models.
  • Developers studying or adapting question-answering training workflows.
  • Practitioners evaluating BigBird variants with PyTorch or Flax/JAX.

Documented uses

  • Evaluate PyTorch or Flax BigBird on Natural Questions.
  • Evaluate PyTorch BigBirdPegasus on PubMed.
  • Run inference with BigBird RoBERTa or Pegasus.
  • Prepare Natural Questions data and train BigBird variants with task-specific heads.
Before you choose

Requirements & limits

The repository is public and released under the MIT license.

  • The project provides implementation materials and scripts, not just model descriptions.
  • Natural Questions preparation requires a large download and approximately 250 GB of free disk space.
  • Training paths differ: the PyTorch example uses distributed training across two GPUs, while the Flax script is documented for TPU or GPU use.
  • The README links to a Hugging Face Space demo and Colab notebooks for evaluation and inference.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare Natural Questions data for BigBird training

Set up the repository and create the Natural Questions training and validation files.

Before you start

  • Python 3 and pip
  • A good internet connection
  • About 250 GB of free disk space; dataset download is about 100 GB

Steps

  1. Clone the repository, install its requirements with pip3 install -r requirements.txt, and change into src.

  2. Create the data directory with mkdir -p data.

  3. Run PROCESS_TRAIN=true python3 prepare_natural_questions.py to prepare data/nq-training.jsonl.

  4. Run PROCESS_TRAIN=false python3 prepare_natural_questions.py to prepare data/nq-validation.jsonl.

Expected outcome

Training and validation JSONL files are prepared for the repository’s Natural Questions workflows.

Guide limitations

  • The README estimates about three hours for data preparation.
  • The stated disk-space requirement is about 250 GB free, in addition to a good internet connection.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
thevasudevgupta/bigbird · Official homepage title · retained in its source language
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