https://github.com/sanyam5/skip-thoughts

The first public PyTorch implementation of Skip-Thought Vectors
https://github.com/sanyam5/skip-thoughts

Last synced: 25 days ago
JSON representation

The first public PyTorch implementation of Skip-Thought Vectors

• Host: GitHub
• URL: https://github.com/sanyam5/skip-thoughts
• Owner: sanyam5
• License: mit
• Created: 2017-12-17T06:17:30.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-19T07:18:08.000Z (over 7 years ago)
• Last Synced: 2024-11-14T05:34:29.669Z (7 months ago)
• Language: Python
• Homepage:
• Size: 27.3 KB
• Stars: 226
• Watchers: 7
• Forks: 47
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-pytorch-list-CNVersion - skip-thoughts - Thought词向量模型。 (Tutorials & books & examples｜教程 & 书籍 & 示例 / Other libraries｜其他库:)
• Awesome-pytorch-list - skip-thoughts - Thought Vectors in PyTorch. (Tutorials, books, & examples / Other libraries:)

README

        
# skip-thoughts

An implementation of Skip-Thought Vectors in PyTorch.

### Blog

Here's a [blog explaining the subtleties of Skip-Thoughts](http://sanyam5.github.io/my-thoughts-on-skip-thoughts/)

![blog](http://sanyam5.github.io/images/skip-thoughts/skip-rnn.png)

![blog](http://sanyam5.github.io/images/skip-thoughts/impute-rnn.png)

Instructions

------------

### Training

* Download [BookCorpus](http://yknzhu.wixsite.com/mbweb) or any other data-set and concatenate all sentences into one file and put it in `./data/` directory

* Modify the following line in `Train.ipynb` notebook accordingly:

`d = DataLoader("./data/dummy_corpus.txt")`

* There is no early stopping. 

* The `Train` notebook runs at the rate of 1 epoch / 2 days on an Nvidia 1080 Ti. 

* Your model is saved when `./saved_models` when the average training loss in the last 20 iterations dips below the previous best.

### Evaluation

Only implemented on classification tasks

* Download the [movie review dataset](http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz) and put `rt-polarity.neg` and  `rt-polarity.pos` in the `./tasks/mr_data` directory.

* You may also test on other classification tasks by downloading the datasets and providing their path and tasks type in `Evaluate.ipynb`