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README.md

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----
-license: mit
----
+---
+language:
+- ur
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- roberta
+- urdu
+- hate-speech
+- sequence-classification
+- pytorch
+- safety
+- moderation
+license: other  # Update to the correct license for your model/checkpoint
+---
+
+## Urdu RoBERTa Hate Speech Classifier
+
+- **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 to detect hate speech. It is intended for content moderation, research, and educational uses. Do not use as the sole basis for enforcement or punitive actions.
+
+### Intended uses and limitations
+
+- Intended:
+  - Flagging potentially hateful content in Urdu text (e.g., tweets, comments)
+  - Assisting human moderators and analysts
+  - Research and educational demos
+- Limitations:
+  - May misclassify satire, reclaimed slurs, or dialectal expressions
+  - Sensitive to domain shift (platform/topic/user community)
+  - Biases may reflect the data it was trained on
+- Risks:
+  - False positives can suppress legitimate speech
+  - False negatives can miss harmful content
+- Mitigations:
+  - Use with a human-in-the-loop
+  - Monitor performance and update thresholds per deployment domain
+
+### How to use (Transformers)
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+MODEL_ID = "your-username/urdu-roberta-hate"  # replace with your repo id
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+model.eval()
+
+def predict_label(text: str) -> dict:
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = outputs.logits.softmax(dim=-1).squeeze().tolist()
+        pred_id = int(outputs.logits.argmax(dim=-1).item())
+        id2label = model.config.id2label
+        return {
+            "label_id": pred_id,
+            "label": id2label.get(str(pred_id), id2label.get(pred_id, str(pred_id))),
+            "scores": {"not_hate": probs[0], "hate": probs[1]},
+        }
+
+print(predict_label("یہ نفرت انگیز مواد ہے یا نہیں؟"))
+```
+
+Or with a pipeline:
+
+```python
+from transformers import pipeline
+clf = pipeline("text-classification", model="your-username/urdu-roberta-hate", top_k=None)
+print(clf("یہ نفرت انگیز مواد ہے یا نہیں؟"))
+```
+
+### Inference API (no code download)
+
+- Python (requests):
+
+```python
+import os, requests
+
+API_URL = "https://api-inference.huggingface.co/models/your-username/urdu-roberta-hate"
+HEADERS = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN', '')}"}
+
+def infer(text: str):
+    r = requests.post(API_URL, headers=HEADERS, json={"inputs": text}, timeout=30)
+    r.raise_for_status()
+    return r.json()  # [{label, score}, ...] OR [[{label, score}, ...]] depending on config
+
+print(infer("یہ نفرت انگیز مواد ہے یا نہیں؟"))
+```
+
+- cURL:
+
+```bash
+curl -X POST \
+  -H "Authorization: Bearer $HF_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{"inputs":"یہ نفرت انگیز مواد ہے یا نہیں؟"}' \
+  https://api-inference.huggingface.co/models/your-username/urdu-roberta-hate
+```
+
+- huggingface_hub client:
+
+```python
+from huggingface_hub import InferenceClient
+client = InferenceClient(model="your-username/urdu-roberta-hate", token=os.environ.get("HF_TOKEN"))
+print(client.text_classification("یہ نفرت انگیز مواد ہے یا نہیں؟"))
+```
+
+### Expected input and output
+
+- Input: a single Urdu string (short to medium-length, e.g., tweet or comment)
+- Output:
+  - Transformers: logits or labels via pipeline
+  - Recommended mapping:
+    - `id2label = {"0": "not_hate", "1": "hate"}`
+    - `label2id = {"not_hate": 0, "hate": 1}`
+
+If you want 0/1 numeric outputs in an API, map `label` to `{not_hate: 0, hate: 1}`.
+
+### Preprocessing
+
+- Standard RoBERTa tokenization (`AutoTokenizer` for the base model).
+- Truncation and padding to the model max length (e.g., 128/256). Adjust as needed.
+
+### Training details
+
+- Base: `urduhack/roberta-urdu-small`
+- Objective: Cross-entropy, 2 classes
+- Hardware: CPU or single GPU
+- Hyperparameters (example; update with your actual settings):
+  - lr: 2e-5
+  - batch_size: 16
+  - epochs: 3–5
+  - max_length: 128–256
+  - weight_decay: 0.01
+  - warmup_ratio: 0.1
+
+### Data
+
+- Source: Custom Urdu hate speech dataset (e.g., tweets/comments)
+- Class balance: Please document distribution if available (helps threshold setting)
+- Cleaning: Standard text normalization as applicable
+
+### Evaluation
+
+- Metrics to report (fill in your numbers):
+  - Accuracy: TBD
+  - F1 (macro): TBD
+  - Precision/Recall (hate class): TBD
+- Suggested threshold: argmax for 2-class; for imbalanced data, consider probability threshold tuning on a validation set.
+
+### Limitations and bias
+
+- May misinterpret context, irony, or reclaimed language
+- Potential domain and demographic bias
+- Performance can degrade on long-form or code-mixed content
+
+### Responsible AI and safety
+
+- Use as an assistive tool with human review
+- Provide user appeals and error reporting
+- Regularly audit for disparities
+
+### Deployment tips
+
+- Direct load in Python: `from_pretrained("your-username/urdu-roberta-hate")`
+- Render/Flask: set `MODEL_ID` to this repo id and load via `AutoTokenizer/AutoModelForSequenceClassification.from_pretrained(MODEL_ID)`
+- HF Inference API: use bearer token for private repos or higher rate limits
+- HF Space: create a Docker Space exposing `/predict` for a custom API interface
+
+### License
+
+- The license must be compatible with the base model and your data usage. Update the `license:` field above and add details here.
+
+### Citation
+
+If you use this model, please cite the base model and your fine-tuning work.
+
+```bibtex
+@misc{urdu_roberta_hate_2025,
+  title  = {Urdu RoBERTa Hate Speech Classifier},
+  author = {Your Name},
+  year   = {2025},
+  howpublished = {\url{https://huggingface.co/your-username/urdu-roberta-hate}}
+}
+```
+
+### Acknowledgements
+
+- Base model: `urduhack/roberta-urdu-small`
+- Libraries: 🤗 Transformers, PyTorch