https://github.com/NoviScl/BERT-RACE

https://github.com/NoviScl/BERT-RACE

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/NoviScl/BERT-RACE
• Owner: NoviScl
• Created: 2019-01-23T13:29:33.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-10-22T20:19:09.000Z (about 2 years ago)
• Last Synced: 2024-08-02T08:09:52.610Z (4 months ago)
• Language: Python
• Size: 104 KB
• Stars: 79
• Watchers: 5
• Forks: 21
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-bert - NoviScl/BERT-RACE - pretrained-BERT). I adapted the original BERT model to work on multiple choice machine comprehension. (BERT QA & RC task:)

README

        
# BERT for RACE

By: Chenglei Si (River Valley High School)

### Update:

XLNet has achieved impressive gains on RACE recently. You may refer to my other repo: https://github.com/NoviScl/XLNet_DREAM to see how to use XLNet for multiple-choice machine comprehension problems. Huggingface has updated their work [pytorch_trainsformers](https://github.com/huggingface/pytorch-transformers), please refer to their repo for the documentation and more details of the new version. 

### Implementation

This work is based on Pytorch implementation of BERT (https://github.com/huggingface/pytorch-pretrained-BERT). I adapted the original BERT model to work on multiple choice machine comprehension.

### Environment:

The code is tested with Python3.6 and Pytorch 1.0.0.

### Usage

1. Download the dataset and unzip it. The default dataset directory is ./RACE

2. Run ```./run.sh```

### Hyperparameters

I did some tuning and find the following hyperparameters to work reasonally well:

BERT_base: batch size: 32, learning rate: 5e-5, training epoch: 3

BERT_large: batch size: 8, learning rate: 1e-5 (DO NOT SET IT TOO LARGE), training epoch: 2

### Results

Model | RACE | RACE-M | RACE-H 

--- | --- | --- | --- |

BERT_base | 65.0 | 71.7 | 62.3 

BERT_large | 67.9 | 75.6 | 64.7

You can compare them with other results on the [leaderboard](http://www.qizhexie.com/data/RACE_leaderboard).

BERT large achieves the current (Jan 2019) best result. Looking forward to new models that can beat BERT!

### More Details

I have written a short [report](./BERT_RACE.pdf) in this repo describing the details.