t5-small-ssm Open-Source AI Model - Achieve Closed-Book Q&A Practical Functions Without External Knowledge Sources

T5 Small Ssm

Developed by google

Google's T5 model achieves closed-book QA through pre-training, capable of answering questions without relying on external knowledge sources

Large Language Model EnglishOpen Source License:Apache-2.0 #Closed-book QA #Implicit knowledge retrieval #No external knowledge sources

Downloads 88

Release Time : 3/2/2022

Model Overview

Based on the T5 architecture, this model is first pre-trained on the C4 dataset with denoising objectives, then additionally pre-trained on Wikipedia data using REALM's salient span masking objective, specifically designed for closed-book QA scenarios

Model Features

Closed-book QA capability

Can answer questions without external knowledge sources, with answers entirely derived from knowledge stored in the model's internal parameters

Dual pre-training strategy

First pre-trained on C4 dataset with standard denoising objectives, then knowledge-intensive pre-training on Wikipedia using REALM's salient span masking objectives

Scalability

Research shows model performance improves with scale, with larger versions performing comparably to open-domain QA systems

Model Capabilities

Closed-book QA

Knowledge retrieval

Text generation

Use Cases

Education

Knowledge QA system

Building automated QA systems that don't rely on external databases

Can achieve accuracy comparable to explicit retrieval systems

Research

Knowledge encapsulation research

Studying how much knowledge can be encapsulated in language model parameters

Validated the positive correlation between model scale and knowledge storage capacity

🚀 Google's T5 for Closed Book Question Answering

This project utilizes Google's T5 for Closed Book Question Answering. It pre - trains the model on C4 and Wikipedia, offering a novel approach to question - answering without relying on external context during inference.

🚀 Quick Start

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

✨ Features

The model was pre - trained using T5's denoising objective on C4.
Subsequently, it was additionally pre - trained using REALM's salient span masking objective on Wikipedia.
It can implicitly store and retrieve knowledge using natural language queries, and its performance scales with model size, competing well with open - domain systems.

📦 Installation

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

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 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.

model image

Other Information

Other Community Checkpoints: here
Paper: How Much Knowledge Can You Pack Into the Parameters of a Language Model?
Authors: Adam Roberts, Colin Raffel, Noam Shazeer

🔧 Technical Details

No specific technical implementation details (more than 50 words) are provided in the original document, so this section is skipped.

📄 License

The project is licensed under the Apache - 2.0 license.

⚠️ Important Note

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

Property	Details
Datasets	C4, Wikipedia
License	Apache - 2.0

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