From 5e151f5e7771b89989c9d553c2b014d84770a1c3 Mon Sep 17 00:00:00 2001 From: LysandreJik Date: Fri, 6 Sep 2019 12:08:36 -0400 Subject: [PATCH] Table of contents --- examples/README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/examples/README.md b/examples/README.md index 46ff9270db..a2236cf0f3 100644 --- a/examples/README.md +++ b/examples/README.md @@ -3,6 +3,13 @@ In this section a few examples are put together. All of these examples work for several models, making use of the very similar API between the different models. +| Section | Description | +|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------| +| [Language Model fine-tuning](#language-model-fine-tuning) | Fine-tuning the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. | +| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. | +| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. | +| [SQuAD](#squad) | Using BERT for question answering, examples with distributed training. | + ## Language model fine-tuning Based on the script `run_lm_finetuning.py`.