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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.
| Date | Stars |
|---|---|
| 2026-07-24 | 1553 |
| 2026-07-25 | 1553 |
| 2026-07-28 | 1553 |
| 2026-07-30 | 1553 |
| 2026-07-31 | 1553 |
| 2026-08-06 | 1554 |
Today
+1 stars today
This week
+1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.06%/day
### ------ Update September 2023 ------ The online demo is no longer available as it's not possible for us to renew the certificate. The code in this repo still works, but you might have to make some changes for it to work in Python 3 (see the open PRs). You can also check out the PyTorch version of this algorithm called [torchMoji](https://github.com/huggingface/torchMoji) made by HuggingFace. # DeepMoji [](https://www.youtube.com/watch?v=u_JwYxtjzUs) *(click image for video demonstration)* DeepMoji is a 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. See the [paper](https://arxiv.org/abs/1708.00524) or [blog post](https://medium.com/@bjarkefelbo/what-can-we-learn-from-emojis-6beb165a5ea0) for more details. ## Overview * [deepmoji/](deepmoji) contains all the underlying code needed to convert a dataset to our vocabulary and use our model. * [examples/](examples) contains short code snippets showing how to convert a dataset to our 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 our [paper](https://arxiv.org/abs/1708.00524) if you use our model or code (see below for citation). ## Frameworks This code is based on Keras, which requires either Theano or Tensorflow as the backend. If you would rather use pyTorch there's an implementation available [here](https://github.com/huggingface/torchMoji), which has kindly been provided by Thomas Wolf. ## Installation We assume that you're using [Python 2.7](https://www.python.org/downloads/) with [pip](https://pip.pypa.io/en/stable/installing/) installed. As a backend you need to install either [Theano (version 0.9+)](http://deeplearning.net/software/theano/install.html) or [Tensorflow (version 1.3+)](https://www.tensorflow.org/install/). Once that's done you need to run the following inside the root directory to install the remaining dependencies: ```bash pip install -e . ``` This will install the following dependencies: * [Keras](https://github.com/fchollet/keras) (the library was tested on version 2.0.5 but anything above 2.0.0 should work) * [scikit-learn](https://github.com/scikit-learn/scikit-learn) * [h5py](https://github.com/h5py/h5py) * [text-unidecode](https://github.com/kmike/text-unidecode) * [emoji](https://github.com/carpedm20/emoji) Ensure that Keras uses your chosen backend. You can find the instructions [here](https://keras.io/backend/), under the *Switching from one backend to another* section. Run the included script, which downloads the pretrained DeepMoji weights (~85MB) from [here](https://www.dropbox.com/s/xqarafsl6a8f9ny/deepmoji_weights.hdf5?dl=0) and places 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 nosetests -v ``` By default, this will also run finetuning tests. These tests train the model for one epoch and then check the
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Stefan Schweter · Germany
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:43f9aabe569241cb, topic:nlp, topic:natural-language-processing, topic:text-classification
matched fp:43f9aabe569241cb, topic:deep-learning, topic:tensorflow