Create README.md

README.md

@@ -0,0 +1,124 @@
+---
+license: apache-2.0
+tags:
+  - fake-news-detection
+  - multimodal
+  - clip
+  - bangla
+  - english
+  - supervised-learning
+  - fact-checking
+  - image-text
+  - misinformation
+  - fine-tuned
+library_name: transformers
+inference: true
+language:
+  - bn
+  - en
+---
+
+# 🛡️ Multimodal BN-EN Fake News Scanner
+
+A **fine-tuned CLIP model for detecting fake news in Bangla-English (BN-EN) content** using **text and image analysis**.
+
+This model was **supervised-trained on real and fake news pairs** to better detect misinformation in South Asian digital content. During inference, it uses **prompt-based similarity** to classify inputs.
+
+Deployed at:  
+🔗 [https://huggingface.co/jarif/Multimodal-BNEN-Fake-News-Scanner-Model](https://huggingface.co/jarif/Multimodal-BNEN-Fake-News-Scanner-Model)
+
+---
+
+## ✅ Key Features
+
+- ✅ **Supervised fine-tuning** on fake/real news dataset
+- 📝 **Bangla + English text support**
+- 🖼️ Analyzes **image authenticity**
+- 🔍 Uses **prompt-based classification** with semantic similarity
+- 🧩 Built on `openai/clip-vit-base-patch32`, fine-tuned for misinformation detection
+- 🌍 Optimized for **South Asian context**
+- 📦 Fully compatible with Hugging Face `transformers`
+
+---
+
+## 💻 Inference Code
+
+Classify **images** and **text** as **Real** or **Fake** and display results in a clean table using `tabulate`.
+
+### Install Dependencies
+
+```
+pip install transformers torch pillow tabulate
+
+from transformers import CLIPModel, CLIPProcessor
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from tabulate import tabulate
+
+# Load your fine-tuned model
+model = CLIPModel.from_pretrained("jarif/Multimodal-BNEN-Fake-News-Scanner-Model")
+processor = CLIPProcessor.from_pretrained("jarif/Multimodal-BNEN-Fake-News-Scanner-Model")
+
+# Define class prompts in Bangla
+class_texts = ["এটি ফেক নিউজ", "এটি রিয়েল নিউজ"]  # ["This is fake news", "This is real news"]
+
+# --- Image Classification ---
+image = Image.open("your_image.jpg").convert("RGB")  # Replace with your image path
+image_inputs = processor(images=image, return_tensors="pt")
+image_emb = model.get_image_features(**image_inputs)
+
+# --- Text Classification ---
+text = "পদ্মা নদীর প্রবল স্রোতে লঞ্চঘাট বিলীন হয়েছে।"
+text_inputs = processor(text=text, return_tensors="pt", padding=True, truncation=True)
+text_emb = model.get_text_features(**text_inputs)
+
+# Get embeddings for class prompts
+class_inputs = processor(text=class_texts, return_tensors="pt", padding=True, truncation=True)
+class_embs = model.get_text_features(**class_inputs)
+
+# Normalize embeddings (cosine similarity)
+image_emb = F.normalize(image_emb, p=2, dim=-1)
+text_emb = F.normalize(text_emb, p=2, dim=-1)
+class_embs = F.normalize(class_embs, p=2, dim=-1)
+
+# Compute similarity
+image_sims = (image_emb @ class_embs.T).squeeze(0)
+text_sims = (text_emb @ class_embs.T).squeeze(0)
+
+# Predict
+image_pred = image_sims.argmax().item()
+text_pred = text_sims.argmax().item()
+
+image_label = "🛑 Fake" if image_pred == 0 else "✅ Real"
+text_label = "🛑 Fake" if text_pred == 0 else "✅ Real"
+
+# Create result table
+table = [
+    ["ImageRelation", image_label],
+    ["Text Relation", text_label]
+]
+
+# Print formatted table
+print(tabulate(table, headers=["Modality", "Prediction"], tablefmt="fancy_grid"))
+```
+
+
+# 📊 Example Output
+```
+╒════════════════════════════════════════════════════════════════════╕
+│                         Modality                │ Prediction │
+╞════════════════════════════════════════════════════════════════════╡
+│ Image Relation                                  │ ✅ Real    │
+│ Text Relation                                   │ 🛑 Fake    │
+╘════════════════════════════════════════════════════════════════════╛
+```
+
+# 📎 License
+- This model is licensed under Apache 2.0.
+- Fine-tuned by Sadik Al Jarif for public misinformation detection.
+
+# 🙌 Acknowledgements
+- OpenAI CLIP
+- Hugging Face Transformers
+- Bangla NLP Community