Xlm Emo T

🚀 XLM-EMO: Multilingual Emotion Prediction Model

XLM-EMO is a multilingual emotion prediction model for social media data. It can help social and computational scientists study people's behavior and reactions to online events, especially useful in the context of low - resource languages.

🚀 Quick Start

This README provides an overview of the XLM - EMO model, including its features, intended use, results, license, and citation information.

✨ Features

• Multilingual Support: The model is trained on emotion detection datasets across 19 languages, enabling it to handle text in multiple languages.
• Zero - shot Performance: It shows competitive performance in a zero - shot setting, which is beneficial for low - resource languages.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

You can use the model for emotion classification as shown in the following examples:

• Emotion Classification 1: "Guarda! ci sono dei bellissimi capibara!"
• Emotion Classification 2: "Sei una testa di cazzo!!"
• Emotion Classification 3: "Quelle bonne nouvelle!"

📚 Documentation

Model

This model is the fine - tuned version of the [XLM - T](https://aclanthology.org/2022.lrec - 1.27/) model.

Intended Use

The model is intended as a research output for research communities.

• Primary intended users: The primary intended users of these models are AI researchers.

Results

This model had an F1 of 0.85 on the test set.

🔧 Technical Details

No detailed technical implementation details are provided in the original document.

📄 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”, 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.

📄 Citation

Please use the following BibTeX entry if you use this model in your project:

@inproceedings{bianchi2021feel,
    title = "{XLM-EMO: Multilingual Emotion Prediction in Social Media Text}",
    author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
    booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    year = "2022",
    publisher = "Association for Computational Linguistics",
}