https://github.com/diego999/selfsent
Implementation in Tensorflow of A Structured Self-attentive Sentence Embedding with the sentiment analysis task.
https://github.com/diego999/selfsent
Last synced: 6 months ago
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Implementation in Tensorflow of A Structured Self-attentive Sentence Embedding with the sentiment analysis task.
- Host: GitHub
- URL: https://github.com/diego999/selfsent
- Owner: Diego999
- Created: 2017-05-15T08:26:57.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-07-15T22:03:13.000Z (over 8 years ago)
- Last Synced: 2025-03-25T15:15:17.975Z (7 months ago)
- Language: Python
- Homepage: https://arxiv.org/abs/1703.03130
- Size: 55.7 KB
- Stars: 66
- Watchers: 6
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SelfAttentiveSentenceEmbedding
Implementation in Tensorflow of [A Structured Self-attentive Sentence Embedding](https://arxiv.org/abs/1703.03130) with the sentiment analysis task.
The code organization is similar to [NeuroNER](https://arxiv.org/abs/1705.05487).
# Requirements
SelfSent relies on Python 3.5 and TensorFlow 1.0+.
For the package `stanford_corenlp_pywrapper`, just install it from https://github.com/mpagli/stanford_corenlp_pywrapper.
# Data
You need to create a `data` folder next to the `src` folder.
Then for each dataset, you have to create a separate folder. In this folder, you just need to put a file `all.json` where each line correspond to a json sample with its attributes.
Here a sample of Yelp dataset:
{"review_id":"IYE_M_cRsk-AhVYeYvnADg","user_id":"r-zUIQPaHzvIyL93wQaoiQ","business_id":"HE23DlZWAO_JF1VIHA60TQ",**"stars":3**,"date":"2012-10-09",**"text":"This is the Capitol Square branch."**,"useful":0,"funny":0,"cool":0,"type":"review"}
In the case of review star prediction, the needed attributes are `text` and `stars`.
{"review_id":"IYE_M_cRsk-AhVYeYvnADg","stars":3,"date":"2012-10-09","text":"This is the Capitol Square branch.","user_id":"r-zUIQPaHzvIyL93wQaoiQ","business_id":"HE23DlZWAO_JF1VIHA60TQ""useful":0,"funny":0,"cool":0,"type":"review"}
The parameters `do_split` (force to split even if the pickle files exist), `training`, `valid`, `test` in `parameters.ini` will split the dataset accordingly.
# Word Embedding
It also needs some word embeddings, which should be downloaded from http://neuroner.com/data/word_vectors/glove.6B.100d.zip, unzipped and placed in `/data/word_vectors`. This can be done on Ubuntu and Mac OS X with:
```
# Download some word embeddings
mkdir -p SelfSent-master/data/word_vectors
cd SelfSent-master/data/word_vectors
wget http://neuroner.com/data/word_vectors/glove.6B.100d.zip
unzip glove.6B.100d.zip
```
# Training
Be sure that `use_pretrained_model = false` and have at least `all.json` in the data folder.
# Deployment
You need to have a pretrained model, which is composed of:
- dataset.pickle
- model.ckpt.data-00000-of-00001
- model.ckpt.index
- model.ckpt.meta
- parameters.ini
Don't forget to put `use_pretrained_model = true` and the path to the pretrained model folder.
# Issues/Pull Requests/Feedbacks
Don't hesitate to contact for any feedback or create issues/pull requests.