Roberta_base_model

Urdu RoBERTa Hate Speech Classifier (Balanced)

• Base model: urduhack/roberta-urdu-small
• Task: Binary text classification (hate vs. not_hate)
• Language: Urdu (ur)
• Labels
  • 0 → not_hate
  • 1 → hate

This model fine-tunes a small RoBERTa for Urdu hate-speech detection. Class imbalance was addressed by oversampling with SMOTE at the feature level (TF–IDF) prior to tokenization-based training.

Training data and preprocessing

• Source dataset: Adnan855570/urdu-hate-speech (Excel files: preprocessed_combined_file (1).xlsx, Urdu_Hate_Speech.xlsx)
• Columns used in notebook: Tweet (text), Tag (label in {0,1})
• Steps:
  • TF–IDF featurization (max_features=10000)
  • SMOTE oversampling (random_state=42) to balance classes
  • Train/test split: 80/20 (random_state=42)
  • Tokenization: AutoTokenizer.from_pretrained("urduhack/roberta-urdu-small") with truncation=True, padding=True

Training setup

• Model: AutoModelForSequenceClassification with num_labels=2
• Device: GPU if available
• Hyperparameters:
  • epochs: 3
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • warmup_steps: 500
  • weight_decay: 0.01
  • evaluation_strategy: epoch
  • save_strategy: epoch
  • load_best_model_at_end: true
• Metrics:
  • Accuracy, Precision, Recall, F1 (binary)

Evaluation results (test split)

• accuracy: 0.7891
• f1: 0.7854
• precision: 0.8208
• recall: 0.7529

Note: Results derive from the balanced (SMOTE) dataset and the 80/20 split used in the notebook.

How to use (Transformers)

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

MODEL_ID = "Adnan855570/urdu-roberta-hate"  # replace if different
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID).eval()

id2label = model.config.id2label or {"0":"not_hate","1":"hate"}

def predict(text: str):
    enc = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        logits = model(**enc).logits
        probs = logits.softmax(dim=-1).squeeze().tolist()
        pred = int(logits.argmax(dim=-1).item())
    return {"label_id": pred, "label": id2label.get(str(pred), str(pred)),
            "scores": {"not_hate": probs[0], "hate": probs[1]}}

print(predict("یہ نفرت انگیز ہے یا نہیں؟"))

Or with a pipeline:

from transformers import pipeline
clf = pipeline("text-classification", model="Adnan855570/urdu-roberta-hate", top_k=None)
print(clf("یہ نفرت انگیز ہے یا نہیں؟"))

Inference API

• cURL

curl -X POST -H "Authorization: Bearer $HF_TOKEN" -H "Content-Type: application/json" \
  -d '{"inputs":"یہ نفرت انگیز ہے یا نہیں؟"}' \
  https://api-inference.huggingface.co/models/Adnan855570/urdu-roberta-hate

• Python

import os, requests
API_URL = "https://api-inference.huggingface.co/models/Adnan855570/urdu-roberta-hate"
HEADERS = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN','')}"}
print(requests.post(API_URL, headers=HEADERS, json={"inputs":"..."}, timeout=30).json())

Intended uses and limitations

• Intended:
  • Flagging potentially hateful Urdu content
  • Assisting human moderation and research
• Limitations:
  • May misclassify satire, reclaimed slurs, or code-mixed content
  • Domain shift sensitivity (platform/community/topic)
• Risks:
  • False positives/negatives; do not use as the sole basis for punitive actions
• Recommendation:
  • Use with human-in-the-loop; periodically audit outcomes and bias

Label mapping

Ensure the config includes:

• id2label = {"0":"not_hate","1":"hate"}
• label2id = {"not_hate":0,"hate":1}

Reproducibility notes

• SMOTE and split seeds: random_state=42
• Tokenization: truncation and padding enabled (no explicit max_length set in notebook)
• Hardware: single GPU (e.g., Colab)

License

• The model derivation should comply with the base model’s license (urduhack/roberta-urdu-small). Set a compatible license here once confirmed.

Citation

@misc{urdu_roberta_hate_balanced_2025,
  title  = {Urdu RoBERTa Hate Speech Classifier (Balanced)},
  author = {Adnan},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/Adnan855570/urdu-roberta-hate}}
}

Acknowledgements

• Base: urduhack/roberta-urdu-small
• Libraries: 🤗 Transformers, Datasets, PyTorch
• Oversampling: SMOTE (imblearn)