From 0d46b1755305085b941baee84d3393ba769f692d Mon Sep 17 00:00:00 2001 From: Praateek Mahajan Date: Wed, 17 Jul 2019 22:50:10 -0700 Subject: [PATCH] Update Readme Incorrect link for `Quick tour: Fine-tuning/usage scripts` --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index b2374a0dc5..63ef948818 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ These implementations have been tested on several datasets (see the example scri |-|-| | [Installation](#installation) | How to install the package | | [Quick tour: Usage](#quick-tour-usage) | Tokenizers & models usage: Bert and GPT-2 | -| [Quick tour: Fine-tuning/usage scripts](#quick-tour-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation | +| [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation | | [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) | Migrating your code from pytorch-pretrained-bert to pytorch-transformers | | [Documentation](https://huggingface.co/pytorch-transformers/) | Full API documentation and more | @@ -118,7 +118,6 @@ tokenizer = tokenizer_class.from_pretrained(pretrained_weights) ``` ## Quick tour of the fine-tuning/usage scripts - The library comprises several example scripts with SOTA performances for NLU and NLG tasks: - `run_glue.py`: an example fine-tuning Bert, XLNet and XLM on nine different GLUE tasks (*sequence-level classification*)