zacanadir/wsbt-sequence-classifier

🧠 BERT-based 4-Class News Classifier

A fine-tuned BERT-base-uncased model for classifying news articles into four categories: World, Sport, Business, and Science & Technology.

📖 Model Overview

This model adapts BERT for multi-class sequence classification.
It was trained on a labeled news dataset with four categories and achieves strong performance on short text snippets such as news headlines or article summaries.

Label ID	Category
0	World
1	Sport
2	Business
3	Science & Technology

⚙️ Training Details

• Base model: bert-base-uncased
• Task: Sequence classification (4 classes)
• Training environment: Google Colab (T4 GPU)
• Max sequence length: 200
• Batch size: 8
• Epochs: 3
• Optimizer: AdamW (lr=2e-5)
• Framework: PyTorch + 🤗 Transformers Trainer API

The dataset was tokenized using AutoTokenizer with truncation and dynamic padding enabled.
Model and tokenizer were saved using save_pretrained() and uploaded to the Hugging Face Hub.

🧩 Usage

You can load and use the model directly with the 🤗 Transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model from the Hub
model_name = "zacanadir/wsbt-sequence-classifier" 
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

MAX_LENGTH = 200

def predict_text(text_list):
    encodings = tokenizer(
        text_list,
        padding=True,
        truncation=True,
        max_length=MAX_LENGTH,
        return_tensors="pt"
    ).to(model.device)

    model.eval()
    with torch.no_grad():
        outputs = model(**encodings)
        preds = torch.argmax(outputs.logits, dim=-1)
    return preds.tolist()

# Example
sample_texts = [
    "Nvidia becomes the first chipmaker to top $5 trillion in market value.",
    "The Raja of Casablanca advances to the semi final of the African Champions' League."
]

label_to_category = {
    0: "World",
    1: "Sport",
    2: "Business",
    3: "Science & Technology"
}

preds = predict_text(sample_texts)
predicted_categories = [label_to_category[int(i)] for i in preds]
print(predicted_categories)

🧾 Output Example
['Business', 'Sport']

🚀 How to Reuse / Fine-tune Further

If you’d like to fine-tune this model on your own dataset:

from transformers import Trainer, TrainingArguments, AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_dataset

tokenizer = AutoTokenizer.from_pretrained("zacanadir/wsbt-sequence-classifier")
model = AutoModelForSequenceClassification.from_pretrained("zacanadir/wsbt-sequence-classifier", num_labels=4)

# Load your dataset and fine-tune

📦 Files Included
File	                                            Description
config.json	                                        Model architecture & label mapping
pytorch_model.bin	                                Trained weights
tokenizer.json, vocab.txt, special_tokens_map.json	Tokenizer files
training_args.bin	                                Trainer configuration (epochs, LR, etc.)

✨ Acknowledgements
Hugging Face Transformers
Google Colab for free GPU training
PyTorch

Trained and deployed with ❤️ using Hugging Face and Google Colab.