results

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

📰 Fake News Detection with Fine-Tuned DistilBERT

This repository contains a fine-tuned version of DistilBERT for fake news detection, trained on the LIAR dataset. The model predicts credibility scores on a scale of 0 to 5, rather than just a binary classification.

This model is a fine-tuned version of distilbert-base-uncased on the fake.csv & true.csv (Binary classification) dataset. It achieves the following results on the evaluation set:

• Loss: 0.0001
• Accuracy: 1.0

Model Overview

Model: Fine-tuned DistilBERT (distilbert-base-uncased)

Dataset: fake.csv & true.csv (Binary classification)

Labels:

0 → Real News 📰

1 → Fake News 🚨

Use Case: Detect misinformation and classify news as real or fake Deployment: Hosted on Hugging Face and accessible via FastAPI

🔗 https://huggingface.co/anup069/results

Intended uses & limitations

✅ Intended Uses

Fake News Detection: Classifies news articles as FAKE (1) or REAL (0).

Misinformation Analysis: Helps identify misleading or fabricated news.

News Verification Tools: Can be integrated into fact-checking websites, browser extensions, or news aggregator platforms.

Educational Purposes: Useful for research and academic projects on NLP and misinformation detection.

More information needed

⚠️ Limitations

Limited Dataset: Trained only on fake.csv and true.csv, which may not represent all news sources.

Context Understanding: The model classifies based on text patterns, not fact-checking against external sources.

Adversarial Attacks: It may be fooled by well-crafted fake news.

Bias & Generalization: Might struggle with different writing styles, languages, or new topics outside its training data.

Continuous Updates Needed: Fake news trends evolve, requiring periodic retraining with fresh data.

Training and evaluation data

The model was trained on a binary classification dataset consisting of:

fake.csv → Contains fake news articles

true.csv → Contains real news articles

🔹 Data Preprocessing

Text Cleaning: Removed special characters, extra spaces, and stopwords.

Tokenization: Used DistilBERT tokenizer to convert text into input tokens.

Label Encoding: Assigned 1 for fake news and 0 for real news.

Train-Test Split: 80% for training, 20% for evaluation.

📈 Evaluation Metrics

The model was evaluated using:

Accuracy

Precision, Recall, F1-score

Loss (Cross-Entropy Loss)

Training procedure

🔹 Training Process

Loaded fake.csv and true.csv → Preprocessed the text

Used Hugging Face’s Trainer API to fine-tune DistilBERT

Monitored validation loss & accuracy after each epoch

Saved the best-performing model to Hugging Face

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

🚀 API Deployment with FastAPI

The model is already deployed using FastAPI for real-time predictions.

🔗 API Endpoint

📌 Base URL: https://anup069-fake-news-detection-api.hf.space/verify/

🛠 How to Use the API

You can send a POST request to the API to classify news as FAKE (1) or REAL (0).

Example Request:

curl -X POST "https://anup069-fake-news-detection-api.hf.space/verify/"
-H "Content-Type: application/json"
-d '{"text": "Breaking news: Scientists discover a new planet!"}'

Example Response:

{ "label": "FAKE", "score": 0.98 }

✅ Features

✔️ Real-time inference with low latency

✔️ Scalable & FastAPI-based backend

✔️ Can be integrated into web apps, Chrome extensions, or other platforms

Framework versions

• Transformers 4.48.2
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0