Edit model card

Google's T5 Pretraining Dataset: C4 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


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. model image

Model series

This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.

Gpt models

Swedish Gpt


Swedish gpt wiki


Nordic gpt wiki


Dansk gpt wiki


Norsk gpt wiki


Roberta models

Nordic Roberta Wiki


Swe Roberta Wiki Oscar


Roberta Swedish Scandi


Roberta Swedish


Swedish T5 model


Downloads last month

Dataset used to train birgermoell/t5-base-swedish