bigbird pegasus large bigpatent
googleIntroduction
The BigBird-Pegasus model is a large, sparse-attention-based transformer designed to handle longer sequences efficiently. It extends models like BERT to manage sequences up to 4096 tokens, making it suitable for tasks like document summarization and question answering with long contexts. The model was introduced in the paper "Big Bird: Transformers for Longer Sequences" and is available in the Hugging Face model hub.
Architecture
BigBird employs block sparse attention instead of the traditional full attention mechanism used by models like BERT. This approach significantly reduces computational costs, allowing the model to process much longer sequences without sacrificing performance. The model achieves state-of-the-art results in tasks requiring the processing of extended text sequences.
Training
The provided model checkpoint has been fine-tuned for summarization using the BigPatent dataset. The training involves adapting the BigBirdPegasusForConditionalGeneration model to handle long sequences efficiently by leveraging its block sparse attention mechanism.
Guide: Running Locally
To run the BigBird-Pegasus model locally, follow these steps:
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Install Dependencies: Ensure you have the
transformers
library installed.pip install transformers
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Load the Model and Tokenizer:
from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-bigpatent") model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent")
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Generate Predictions:
text = "Replace me by any text you'd like." inputs = tokenizer(text, return_tensors='pt') prediction = model.generate(**inputs) prediction = tokenizer.batch_decode(prediction)
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Cloud GPU Suggestion: For improved performance, consider using cloud-based GPUs provided by services like AWS, GCP, or Azure, especially for handling large datasets or long sequences.
License
The BigBird-Pegasus model is licensed under the Apache 2.0 License, allowing for wide usage and adaptation in both commercial and personal projects.