| --- |
| 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. |
