How to Order Coffee in Singapore?
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old-models
Kopitiam - PyTorch RNN Seq2Seq.ipynb
Kopitiam Emptied.ipynb
Kopitiam mit Attention.ipynb
Kopitiam mit Teacher Forcing.ipynb
Kopitiam.ipynb
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README.md
decoder_attention_100_100000_0.5.pkl
decoder_vanilla_100_100000.pkl
decoder_vanilla_100_100000_0.5.pkl
encoder_attention_100_100000_0.5.pkl
encoder_vanilla_100_100000.pkl
encoder_vanilla_100_100000_0.5.pkl
english_vocab.Dictionary.pkl
kopitiam.tsv
singlish_vocab.Dictionary.pkl

README.md

Kopitiam

This is the repository that host the notebook for the Seq2Seq with PyTorch session in Data Science SG meetup in January 2018. The accompanying slides to this notebook repo can be found on https://goo.gl/Lu6CxB

Problem

Ordering coffee in Singapore hawker centre and food court could result in some surprises:

Kopitiam

Content

Code:

Pre-trained Models:

  • Vanilla RNN Encoder-Decoder model
    • encoder_vanilla_100_100000.pkl
    • decoder_vanilla_100_100000.pkl
  • Vanilla RNN Encoder-Decoder model with teacher forcing
    • encoder_vanilla_100_100000_0.5.pkl
    • decoder_vanilla_100_100000_0.5.pkl
  • Attention RNN Encoder-Decoder model with teacher forcing
    • encoder_attention_100_100000_0.5.pkl
    • decoder_attention_100_100000_0.5.pkl

Requirements

Python 3.6 (preferrably), otherwise Python3 should work too...

gensim==3.2.0
nltk==3.2.5
pandas==0.22.0
torch==0.3.0.post4
torchvision==0.2.0

Acknowledgements

The dataset used in this exercise is hosted on https://www.kaggle.com/alvations/sg-kopi

The materials of this notebook and the accompanying slides are largely based on the