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  base_model:
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  - answerdotai/ModernBERT-base
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  pipeline_tag: text-classification
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  base_model:
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  - answerdotai/ModernBERT-base
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  pipeline_tag: text-classification
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+ metrics:
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+ - accuracy
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+ ---
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+
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+ # ModernBERT-FakeNewsClassifier
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+
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+ ## Model Description
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+
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+ **ModernBERT-FakeNewsClassifier** is a fine-tuned version of [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base), optimized for the binary classification task of detecting fake news. This model processes news articles, including their titles, text content, subject, and publication date, to classify them as either **real (1)** or **fake (0)**. The model is fine-tuned on a dataset containing over 30,000 labeled examples, achieving high accuracy and robustness.
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+
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+ ### Key Features:
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+ - **Base Model**: ModernBERT, designed for long-context processing (up to 8,192 tokens).
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+ - **Task**: Binary classification for fake news detection.
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+ - **Architecture Highlights**:
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+ - Rotary Positional Embeddings (RoPE) for long-context support.
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+ - Local-global alternating attention for memory efficiency.
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+ - Flash Attention for optimized inference speed.
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+
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+ ## Dataset
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+
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+ The dataset used for fine-tuning comprises over 30,000 examples, with the following features:
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+ - **Title**: The headline of the news article.
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+ - **Text**: The main body of the article.
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+ - **Subject**: The category or topic of the article (e.g., Politics, Health).
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+ - **Date**: The publication date of the article.
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+ - **Label**: Binary labels indicating whether the article is fake (`0`) or real (`1`).
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+
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+ ## Notebook: Training and Fine-Tuning
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+ The repository includes the code.ipynb file, which provides:
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+
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+ - Step-by-step instructions for preprocessing the dataset.
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+ - Fine-tuning the ModernBERT model for binary classification.
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+ - Code for evaluating the model using metrics such as accuracy, F1-score, and AUC-ROC.
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+ - You can directly open and run the notebook to replicate or customize the training process.
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+
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+
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+ ## Citation
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+
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+ If you use this model in your research or applications, please cite:
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+
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+ ```
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+ @misc{ModernBERT-FakeNewsClassifier,
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+ author = {Daksh Rathi},
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+ title = {ModernBERT-FakeNewsClassifier: A Transformer-Based Model for Fake News Detection},
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+ year = {2024},
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+ url = {https://huggingface.co/dakshrathi/ModernBERT-base-FakeNewsClassifier},
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+ }
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+