Create README.md

README.md

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+Here's your model card based on the example you provided:  
+
+---
+
+# RoBERTa-Base for News Classification (FP16 Quantized)  
+This is a RoBERTa-Base model fine-tuned on the **AG News dataset** for text classification. It categorizes news articles into one of four classes: **World, Sports, Business, and Science/Technology**. The model has been further **quantized to FP16** for improved inference speed and reduced memory usage, making it efficient for deployment on resource-constrained environments.  
+
+---
+
+## **Model Details**  
+
+### **Model Description**  
+- **Model Type:** Transformer-based text classifier  
+- **Base Model:** `roberta-base`  
+- **Fine-Tuned Dataset:** AG News  
+- **Maximum Sequence Length:** 512 tokens  
+- **Output:** One of four news categories  
+- **Task:** Text classification  
+---
+
+## **Full Model Architecture**  
+```python
+RobertaForSequenceClassification(
+  (roberta): RobertaModel(
+    (embeddings): RobertaEmbeddings(...)
+    (encoder): RobertaEncoder(...)
+  )
+  (classifier): RobertaClassificationHead(
+    (dense): Linear(in_features=768, out_features=768, bias=True)
+    (dropout): Dropout(p=0.1)
+    (out_proj): Linear(in_features=768, out_features=4, bias=True)
+  )
+)
+```
+
+---
+
+## **Usage Instructions**  
+
+### **Installation**  
+```bash
+pip install -U transformers torch
+```
+
+### **Loading the Model for Inference**  
+```python
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "AventIQ-AI/Roberta-Base-News-Classification"  # Update with your model ID
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+model = RobertaForSequenceClassification.from_pretrained(model_name)
+
+# Move to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Function to predict category
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=512).to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+    class_labels = {0: "World", 1: "Sports", 2: "Business", 3: "Science/Technology"}
+    return class_labels[predicted_class]
+
+# Example usage
+custom_text = "Stock prices are rising due to global economic recovery."
+predicted_label = predict(custom_text)
+print(f"Predicted Category: {predicted_label}")
+```
+
+---
+
+## **Training Details**  
+
+### **Training Dataset**  
+- **Name:** AG News  
+- **Size:** 50,000 training samples, 7,600 test samples  
+- **Labels:**  
+  - **0:** World  
+  - **1:** Sports  
+  - **2:** Business  
+  - **3:** Science/Technology  
+
+---
+
+## **Training Hyperparameters**  
+### **Non-Default Hyperparameters:**  
+- **per_device_train_batch_size:** 8  
+- **per_device_eval_batch_size:** 8  
+- **gradient_accumulation_steps:** 2 (effective batch size = 16)  
+- **num_train_epochs:** 3  
+- **learning_rate:** 2e-5  
+- **fp16:** True (for reduced memory footprint and faster inference)  
+- **weight_decay:** 0.01  
+- **optimizer:** AdamW  
+
+---
+
+## **Model Performance**  
+| Metric  | Score |  
+|---------|-------|  
+| Accuracy | **94.3%** |  
+| F1 Score | **94.1%** |  
+| Precision | **94.5%** |  
+| Recall | **94.2%** |  
+
+*(Update these values based on your actual evaluation results.)*  
+
+---
+
+## **Quantization Details**  
+- The model has been quantized to **FP16** to reduce its size and improve inference speed.
+- FP16 quantization provides a **2x reduction in memory** while maintaining similar accuracy.
+
+---
+
+## **Limitations & Considerations**  
+- The model is **trained on AG News** and may not generalize well to **other domains** such as medical, legal, or entertainment news.
+- Due to **FP16 quantization**, there might be a minor loss in precision, but inference speed is significantly improved.
+- The model is **not intended for real-time misinformation detection**—it only classifies text based on its most probable category.
+
+---