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🚀 Google's T5 for Closed Book Question Answering

Google's T5 model tailored for Closed Book Question Answering, leveraging pre - training on diverse datasets to enable knowledge retrieval without external context.

🚀 Quick Start

This model is Google's T5 designed for Closed Book Question Answering. It was first pre - trained using T5's denoising objective on C4 and then further pre - trained using REALM's salient span masking objective on Wikipedia.

⚠️ Important Note

This model should be fine - tuned on a question answering downstream task before it is useable for closed book question answering.

Other Community Checkpoints can be found here.

The related paper is How Much Knowledge Can You Pack Into the Parameters of a Language Model? by Adam Roberts, Colin Raffel, Noam Shazeer.

📚 Documentation

Abstract

It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine - tuning pre - trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open - domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5 - cbqa.

📄 License

This model is released under the apache - 2.0 license.

📦 Additional Information