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tftransformers / t5-small
Model's Last Updated: October 25 2021
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Introduction of t5-small
Model Details of t5-small
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
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 Corpusâ€Â, 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.
Usage
from tf_transformers.models import T5Model
# Any T5 model (t5-small, t5-base, t5-large etc)
model_name = 't5-small'
model = T5Model.from_pretrained(model_name)
Runs of tftransformers t5-small on huggingface.co
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More Information About t5-small huggingface.co Model
Url of t5-small
t5-small huggingface.co Url
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Other API from tftransformers
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