T5 Xxl Ssm Nq

🚀 Google's T5 for Closed Book Question Answering

This project utilizes Google's T5 for Closed Book Question Answering. It offers a solution to answer questions without relying on external context, leveraging the knowledge stored in the model's parameters.

📦 Key Information

🚀 Quick Start

The model was pre - trained using T5's denoising objective on C4, then additionally pre - trained using REALM's salient span masking objective on Wikipedia, and finally fine - tuned on Natural Questions (NQ).

Note: The model was fine - tuned on 100% of the train splits of Natural Questions (NQ) for 10k steps.

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.

📊 Results on Natural Questions - Test Set

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-xxl-ssm-nq")
t5_tok = AutoTokenizer.from_pretrained("google/t5-xxl-ssm-nq")

input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
gen_output = t5_qa_model.generate(input_ids)[0]

print(t5_tok.decode(gen_output, skip_special_tokens=True))

📚 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.