T5 11b Ssm Nq

🚀 Google's T5 for Closed Book Question Answering

This project utilizes Google's T5 for Closed Book Question Answering. It aims to answer questions without relying on external context, leveraging the knowledge implicitly stored in the pre - trained model.

🚀 Quick Start

The model was pre - trained using T5's denoising objective on C4. Then, it was additionally pre - trained using REALM's salient span masking objective on Wikipedia. Finally, it was 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.

✨ Features

• Knowledge Retrieval: The model can implicitly store and retrieve knowledge from the pre - trained parameters to answer questions.
• Scalability: Performance scales with the model size, as shown in the test results.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The model can be used as follows for closed book question answering:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-nq")
t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-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

Results on Natural Questions - Test Set

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

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

The model is licensed under the apache - 2.0 license.