license: mit
language:
- en
- it
- es
base_model:
- microsoft/mdeberta-v3-base
pipeline_tag: text-classification
metrics:
- accuracy
library_name: transformers
tags:
- sentiment-analysis
- 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 text sentiment classification. It is capable of distinguishing into three categories: positive, negative, and neutral
The model is based on the pre-trained version of DeBERTa-v3-large from Microsoft and has been fine-tuned on a sentiment analysis dataset to adapt to recognizing emotions in text.
Usage
You can use the GordonAI to predict the sentiment of a text. The analyzer classifies texts as positive, negative, or neutral.
from transformers import pipeline
# Load the pipeline for text classification
classifier = pipeline("text-classification", model="VinMir/GordonAI-sentiment_analysis")
# Use the model to classify the sentiment of a text
result = classifier("I love this!")
print(result)
Requirements
Python >= 3.9 transformers torch
You can install the dependencies using:
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.