---
license: mit
language:
- en
- it
- es
base_model:
- microsoft/mdeberta-v3-base
pipeline_tag: text-classification
metrics:
- accuracy
library_name: transformers
tags:
- fact-checking
- 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 specifically for fact-checking tasks. It classifies text into one of four categories: **Disinformation**, **Hoax**, **FakeNews**, or **TrueNews**.

Based on the pre-trained mdeberta-v3-base model from Microsoft, it has been fine-tuned on a specialized fact-checking dataset to accurately identify whether a statement is true or false, and to detect misleading or fabricated information.

## Usage

You can use the `GordonAI` to classify texts helping to identify whether a statement is reliable or misleading.

```python
from transformers import pipeline

# Load the pipeline for text classification
classifier = pipeline("text-classification", model="VinMir/GordonAI-fact_checking")

# Use the model to classify text
result = classifier("The Earth is flat.")
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.