Federico Bianchi • Debora Nozza • Dirk Hovy Abstract Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM EMO. The model shows competitive performance in a zero shot setting, suggesting it is helpful in the context of low resource languages. We release our model to the community so that interested researchers can directly use it. Model This model is the fine tuned version of the XLM T model. Intended Use The model is intended as a research output for research communities. Primary intended uses The primary intended users of these models are AI researchers. Results This model had an F1 of 0.85 on the test set. License For models, restrictions may apply to the data (which are derived from existing datasets) or Twitter (main data source). We refer users to the original licenses accompanying each dataset and Twitter regulations. THE SOFTWARE IS PROVIDED “AS IS”, W…
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