--- license: mit language: - en - it - es base_model: - microsoft/mdeberta-v3-base pipeline_tag: text-classification metrics: - accuracy library_name: transformers tags: - emotion-detection - text-classification --- # GordonAI GordonAI is an AI package designed for sentiment analysis, emotion detection, and fact-checking classification. The models are pre-trained on three languages: **Italian**, **English**, and **Spanish**. ## Features This model has been trained for emotion detection and can categorize text into one of the six basic the six basic emotions defined by Paul Ekman (1992): **Joy**, **Sadness**, **Fear**, **Anger**, **Surprise**, **Disgust**, and **Neutral**. The model is based on the pre-trained version of mdeberta-v3-base from Microsoft and has been fine-tuned on an emotion detection dataset to adapt to recognizing emotional expressions in text.. ## Usage You can use `GordonAI` to predict the emotion of a text. ```python from transformers import pipeline # Load the pipeline for text classification classifier = pipeline("text-classification", model="VinMir/GordonAI-emotion_detection") # Use the model to classify the emotion of a text result = classifier("I love this!") print(result) ``` ## Requirements Python >= 3.9 transformers torch You can install the dependencies using: ```bash pip install transformers torch ``` ## Limitations and bias Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. ## Acknowledgments This package is part of the work for my doctoral thesis. I would like to thank **NeoData** and **Università di Catania** for their valuable contributions to the development of this project.