RahulGanapathy/MisInfo-ChatBot

Model Card for Fake News Detection Model

Model Summary

This is a fine-tuned DistilBERT model for fake news detection. It classifies news articles as either real or fake based on textual content. The model has been trained on a labeled dataset consisting of true and false news articles collected from various sources.

Model Details

Model Description

• —Finetuned from: distilbert-base-uncased
• —Language: English
• —Model type: Transformer-based text classification model
• —License: MIT
• —Intended Use: Fake news detection on social media and news websites

Model Sources

• —Repository: Hugging Face Model Hub
• —Paper (if applicable): N/A
• —Demo (if applicable): N/A

Uses

Direct Use

• —This model can be used to detect whether a given news article is real or fake.
• —It can be integrated into fact-checking platforms, misinformation detection systems, and social media moderation tools.

Downstream Use

• —Can be further fine-tuned on domain-specific fake news datasets.
• —Useful for media companies, journalists, and researchers studying misinformation.

Out-of-Scope Use

• —This model is not designed for generating news content.
• —It may not work well for languages other than English.
• —Not suitable for fact-checking complex claims requiring external knowledge.

Bias, Risks, and Limitations

Risks

• —The model may be biased towards certain topics, sources, or writing styles based on the dataset used for training.
• —There is a possibility of false positives (real news misclassified as fake) or false negatives (fake news classified as real).
• —Model performance can degrade on out-of-distribution samples.

Recommendations

• —Users should not rely solely on this model for determining truthfulness.
• —It is recommended to use human verification and cross-check information from multiple sources.

How to Use the Model

You can load the model using transformers and use it for inference as shown below:

from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
import torch

tokenizer = DistilBertTokenizerFast.from_pretrained("your-model-id")
model = DistilBertForSequenceClassification.from_pretrained("your-model-id")

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return "Fake News" if torch.argmax(probs) == 1 else "Real News"

text = "Breaking: Scientists discover a new element!"
print(predict(text))

Training Details

Training Data

The model was trained on a dataset consisting of news articles labeled as real or fake. The dataset includes information from reputable sources and misinformation websites.

Training Procedure

• —Preprocessing:
• —Tokenization using DistilBertTokenizerFast
• —Removal of stop words and punctuation
• —Converting text to lowercase

• —Training Configuration:
• —Model: distilbert-base-uncased
• —Optimizer: AdamW
• —Batch size: 16
• —Epochs: 3
• —Learning rate: 2e-5

Compute Resources

• —Hardware: NVIDIA Tesla T4 (Google Colab)
• —Training Time: ~2 hours

Evaluation

Testing Data

• —The model was evaluated on a held-out test set of 10,000 news articles.

Metrics

• —Accuracy: 92%
• —F1 Score: 90%
• —Precision: 91%
• —Recall: 89%

Results

Environmental Impact

• —Hardware Used: NVIDIA Tesla T4
• —Total Compute Time: ~2 hours
• —Carbon Emissions: Estimated using the ML Impact Calculator

Technical Specifications

Model Architecture

• —The model is based on DistilBERT, a lightweight transformer architecture that reduces computation while retaining accuracy.

Dependencies

• —transformers
• —torch
• —datasets
• —scikit-learn