From 5d5a28d237bca97b2c47a45c2e97c2d6432ed597 Mon Sep 17 00:00:00 2001 From: pkpk Date: Sat, 6 Jul 2019 14:56:50 +0800 Subject: [PATCH] Update README.md (#2716) --- PaddleNLP/Research/MRQA2019-BASELINE/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PaddleNLP/Research/MRQA2019-BASELINE/README.md b/PaddleNLP/Research/MRQA2019-BASELINE/README.md index 029a3ebe..6bb9b59c 100644 --- a/PaddleNLP/Research/MRQA2019-BASELINE/README.md +++ b/PaddleNLP/Research/MRQA2019-BASELINE/README.md @@ -5,7 +5,7 @@ Machine Reading for Question Answering (MRQA), which requires machines to compre Although recent systems achieve impressive results on the several benchmarks, these systems are primarily evaluated on in-domain accuracy. The [2019 MRQA Shared Task](https://mrqa.github.io/shared) focuses on testing the generalization of the existing systems on out-of-domain datasets. In this repository, we provide a baseline for the 2019 MRQA Shared Task that is built on top of [PaddlePaddle](https://github.com/paddlepaddle/paddle), and it features: -* ***Pre-trained Language Model***: [ERNIE](https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE) (Enhanced Representation through kNowledge IntEgration) is a pre-trained language model that is designed to learn better language representations by incorporating linguistic knowledge masking. Our ERNIE-based baseline outperforms the MRQA official baseline that uses BERT by *6.1* point (marco-f1) on the out-of-domain dev set. +* ***Pre-trained Language Model***: [ERNIE](https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE) (Enhanced Representation through kNowledge IntEgration) is a pre-trained language model that is designed to learn better language representations by incorporating linguistic knowledge masking. Our ERNIE-based baseline outperforms the MRQA official baseline that uses BERT by **6.1** point (marco-f1) on the out-of-domain dev set. * ***Multi-GPU Fine-tuning and Prediction***: Support for Multi-GPU fine-tuning and prediction to accelerate the experiments. You can use this repo as starter codebase for 2019 MRQA Shared Task and bootstrap your next model. -- GitLab