Initial commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+# Local data
+/data/local
+
+# Vim swapfiles
+*.swp
+*.swo
+
+# nosetests
+.noseids
+
+# pyTorch model
+pytorch_model.bin
+
+# VSCODE
+.vscode/*
+
+# data
+*.csv

LICENSE

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+MIT License
+
+Copyright (c) 2017 Bjarke Felbo, Han Thi Nguyen, Thomas Wolf
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# torchMoji
+
+TorchMoji is a [pyTorch](http://pytorch.org/) implementation of the [DeepMoji](https://github.com/bfelbo/DeepMoji) model developped by Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan and Sune Lehmann.
+
+This model trained on 1.2 billion tweets with emojis to understand how language is used to express emotions. Through transfer learning the model can obtain state-of-the-art performance on many emotion-related text modeling tasks.
+
+Try the online demo of DeepMoji [http://deepmoji.mit.edu](http://deepmoji.mit.edu/)! See the [paper](https://arxiv.org/abs/1708.00524), [blog post](https://medium.com/@bjarkefelbo/what-can-we-learn-from-emojis-6beb165a5ea0) or [FAQ](https://www.media.mit.edu/projects/deepmoji/overview/) for more details.
+
+## Overview
+* [torchmoji/](torchmoji) contains all the underlying code needed to convert a dataset to the vocabulary and use the model.
+* [examples/](examples) contains short code snippets showing how to convert a dataset to the vocabulary, load up the model and run it on that dataset.
+* [scripts/](scripts) contains code for processing and analysing datasets to reproduce results in the paper.
+* [model/](model) contains the pretrained model and vocabulary.
+* [data/](data) contains raw and processed datasets that we include in this repository for testing.
+* [tests/](tests) contains unit tests for the codebase.
+
+To start out with, have a look inside the [examples/](examples) directory. See [score_texts_emojis.py](examples/score_texts_emojis.py) for how to use DeepMoji to extract emoji predictions, [encode_texts.py](examples/encode_texts.py) for how to convert text into 2304-dimensional emotional feature vectors or [finetune_youtube_last.py](examples/finetune_youtube_last.py) for how to use the model for transfer learning on a new dataset.
+
+Please consider citing the [paper](https://arxiv.org/abs/1708.00524) of DeepMoji if you use the model or code (see below for citation).
+
+## Installation
+
+We assume that you're using [Python 2.7-3.5](https://www.python.org/downloads/) with [pip](https://pip.pypa.io/en/stable/installing/) installed.
+
+First you need to install [pyTorch (version 0.2+)](http://pytorch.org/), currently by:
+```bash
+conda install pytorch -c soumith
+```
+At the present stage the model can't make efficient use of CUDA. See details in the HuggingFace blog post.
+
+When pyTorch is installed, run the following in the root directory to install the remaining dependencies:
+
+```bash
+pip install -e .
+```
+This will install the following dependencies:
+* [scikit-learn](https://github.com/scikit-learn/scikit-learn)
+* [text-unidecode](https://github.com/kmike/text-unidecode)
+* [emoji](https://github.com/carpedm20/emoji)
+
+Then, run the download script to downloads the pretrained torchMoji weights (~85MB) from [here](https://www.dropbox.com/s/q8lax9ary32c7t9/pytorch_model.bin?dl=0) and put them in the model/ directory:
+
+```bash
+python scripts/download_weights.py
+```
+
+## Testing
+To run the tests, install [nose](http://nose.readthedocs.io/en/latest/). After installing, navigate to the [tests/](tests) directory and run:
+
+```bash
+cd tests
+nosetests -v
+```
+
+By default, this will also run finetuning tests. These tests train the model for one epoch and then check the resulting accuracy, which may take several minutes to finish. If you'd prefer to exclude those, run the following instead: 
+
+```bash
+cd tests
+nosetests -v -a '!slow'
+```
+
+## Disclaimer 
+This code has been tested to work with Python 2.7 and 3.5 on Ubuntu 16.04 and macOS Sierra machines. It has not been optimized for efficiency, but should be fast enough for most purposes. We do not give any guarantees that there are no bugs - use the code on your own responsibility!
+
+## Contributions
+We welcome pull requests if you feel like something could be improved. You can also greatly help us by telling us how you felt when writing your most recent tweets. Just click [here](http://deepmoji.mit.edu/contribute/) to contribute.
+
+## License
+This code and the pretrained model is licensed under the MIT license. 
+
+## Benchmark datasets
+The benchmark datasets are uploaded to this repository for convenience purposes only. They were not released by us and we do not claim any rights on them. Use the datasets at your responsibility and make sure you fulfill the licenses that they were released with. If you use any of the benchmark datasets please consider citing the original authors.
+
+## Citation
+```
+@inproceedings{felbo2017,
+  title={Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm},
+  author={Felbo, Bjarke and Mislove, Alan and S{\o}gaard, Anders and Rahwan, Iyad and Lehmann, Sune},
+  booktitle={Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+  year={2017}
+}
+```

data/.gitkeep

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+