AventIQ-AI / Roberta-Base-News-Classification

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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

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

pip install -U transformers torch

Loading the Model for Inference

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

(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.