DeepMoji 是一个模型，接受12亿个带有表情的推文，以了解语言如何表达情绪。 通过转移学习，该模型可以在许多情感相关的文本建模任务上获得最先进的表现。
在 http://deepmoji.mit.edu 尝试我们的在线演示！ 有关详细信息，请参阅论文，博文或常见问题。
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.
Try our online demo at http://deepmoji.mit.edu! See the paper, blog post or FAQ for more details.
deepmoji/ contains all the underlying code needed to convert a dataset to our vocabulary and use our model.
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/ contains code for processing and analysing datasets to reproduce results in the paper.
model/ contains the pretrained model and vocabulary.
data/ contains raw and processed datasets that we include in this repository for testing.
tests/ contains unit tests for the codebase.
To start out with, have a look inside the examples/ directory. See score_texts_emojis.py for how to use DeepMoji to extract emoji predictions, encode_texts.py for how to convert text into 2304-dimensional emotional feature vectors or finetune_youtube_last.py for how to use the model for transfer learning on a new dataset.
Please consider citing our paper if you use our model or code (see below for citation).