T5 Base Lm Adapt

🚀 Google's T5 Version 1.1 - LM-Adapted

Google's T5 Version 1.1 - LM-Adapted is an enhanced model based on the original T5, with several improvements for better performance in natural language processing tasks.

🚀 Quick Start

This README provides detailed information about Google's T5 Version 1.1 - LM-Adapted, including its improvements, pre - training details, and relevant research papers.

✨ Features

Improvements in Version 1.1 - LM Adapted

T5 Version 1.1 - LM Adapted offers the following enhancements compared to the original T5 model:

• Activation Function: It uses GEGLU activation in the feed - forward hidden layer instead of ReLU. Refer to here for more details.
• Dropout: Dropout was turned off during pre - training (resulting in a quality improvement). It should be re - enabled during fine - tuning.
• Pre - training Data: It is pre - trained only on C4 without mixing in downstream tasks.
• Parameter Sharing: There is no parameter sharing between the embedding and classifier layer.
• Model Shapes: "xl" and "xxl" replace "3B" and "11B". The model has a larger d_model and smaller num_heads and d_ff.

This model is pre - trained on both the denoising and language modeling objectives. Specifically, this checkpoint is initialized from T5 Version 1.1 - Base and then trained for an additional 100K steps on the LM objective discussed in the T5 paper. This adaptation enhances the model's ability for prompt tuning.

Note: A well - known fine - tuned version of the T5 Version 1.1 - LM Adapted model is BigScience's T0pp.

Other Information

• Pretraining Dataset: C4
• Other Community Checkpoints: here
• Paper: Exploring the Limits of Transfer Learning with a Unified Text - to - Text Transformer
• Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu

📚 Documentation

Abstract

Transfer learning, where a model is first pre - trained on a data - rich task before being fine - tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text - to - text format. Our systematic study compares pre - training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state - of - the - art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre - trained models, and code.

📄 License

This project is licensed under the Apache 2.0 license.