From 02509d4b06d112bb5d050bf359a8f3e93c907e01 Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Tue, 30 Jun 2020 12:01:11 +0200 Subject: [PATCH] Model Card Fixing (#5371) - Model pic uploaded to a more permanent place --- model_cards/mrm8488/t5-base-finetuned-summarize-news/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model_cards/mrm8488/t5-base-finetuned-summarize-news/README.md b/model_cards/mrm8488/t5-base-finetuned-summarize-news/README.md index 44100690a6..8ca1591889 100644 --- a/model_cards/mrm8488/t5-base-finetuned-summarize-news/README.md +++ b/model_cards/mrm8488/t5-base-finetuned-summarize-news/README.md @@ -15,7 +15,7 @@ The **T5** model was presented in [Exploring the Limits of Transfer Learning wit 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](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67) +![model image](https://i.imgur.com/jVFMMWR.png) ## Details of the downstream task (Summarization) - Dataset 📚