Create README.md

README.md

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+---
+
+language: it
+
+license: mit
+
+tags:
+
+- sentiment
+
+- Italian
+
+---
+
+# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
+
+## Abstract
+
+Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad?
+
+An abundance of approaches has been introduced for tackling both tasks. However, at least for Italian, they all treat only one of the tasks at a time. We introduce FEEL-IT, a novel benchmark corpus of Italian Twitter posts annotated with four basic emotions: anger, fear, joy, sadness. By collapsing them, we can also do sentiment analysis. We evaluate our corpus on benchmark datasets for both emotion and sentiment classification, obtaining competitive results.
+
+We release an open-source Python library, so researchers can use a model trained on FEEL-IT for inferring both sentiments and emotions from Italian text.
+
+| Model | Download |
+
+| ------ | ------ |
+
+| `feel-it-italian-sentiment` | [Link](https://huggingface.co/MilaNLProc/feel-it-italian-sentiment) |
+
+| `feel-it-italian-emotion` | [Link](https://huggingface.co/MilaNLProc/feel-it-italian-emotion) | 
+
+## Model
+
+The feel-it-italian-emotion model performs emotion classification (joy, fear, anger, sadness). We fine-tuned the [UmBERTo model](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on our new dataset (i.e., FEEL-IT) obtaining state-of-the-art performances on different data sets.
+
+## Data
+
+Our data has been collected by annotating tweets from a broad range of topics. In total, we have 2037 tweets annotated with an emotion label. More details can be found in our paper.
+
+## Performance
+
+Soon
+
+## Usage
+
+```python
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-emotion")
+
+model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-emotion")
+
+```
+
+## Citation
+
+Please use the following bibtex entry if you use this model in your project:
+
+```
+
+@inproceedings{bianchi2021feel,
+
+    title = {{"FEEL-IT: Emotion and Sentiment Classification for the Italian Language"}},
+
+    author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
+
+    booktitle = "Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
+
+    year = "2021",
+
+    publisher = "Association for Computational Linguistics",
+
+}
+
+```