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---
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language:
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- he
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license: cc-by-sa-4.0
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tags:
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- text-classification
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- profanity-detection
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- toxicity
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- hebrew
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- bert
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- alephbert
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library_name: transformers
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base_model: onlplab/alephbert-base
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pipeline_tag: text-classification
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datasets:
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- custom
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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---
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# OpenCensor-H1-Mini
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**OpenCensor-H1-Mini** is a lightweight, efficient version of **OpenCensor-H1**, designed to detect profanity, toxicity, and offensive content in Hebrew text. It is fine-tuned on the `onlplab/alephbert-base` architecture.
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## Model Details
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- **Model Name:** OpenCensor-H1-Mini
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- **Base Model:** `onlplab/alephbert-base`
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- **Task:** Binary Classification (0 = Clean, 1 = Toxic/Profane)
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- **Language:** Hebrew
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- **Max Sequence Length:** 256 tokens (optimized for efficiency)
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## Performance
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| Metric | Score |
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| :--- | :--- |
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| **Accuracy** | 0.9826 |
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| **F1-Score** | 0.9823 |
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| **Precision** | 0.9812 |
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| **Recall** | 0.9835 |
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*Note: Best Threshold = 0.17*
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### Training Graphs
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| Validation F1 | Threshold Analysis |
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| :---: | :---: |
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|  |  |
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## How to Use
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You can use this model directly with the Hugging Face `transformers` library.
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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# Load the model
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model_id = "LikoKIko/OpenCensor-H1-Mini"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForSequenceClassification.from_pretrained(model_id).eval()
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def predict(text):
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# Tokenize input
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inputs = tokenizer(
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text,
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return_tensors="pt",
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truncation=True,
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padding=True,
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max_length=256
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)
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# Predict
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with torch.no_grad():
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logits = model(**inputs).logits
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score = torch.sigmoid(logits).item()
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return {
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"text": text,
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"score": round(score, 4),
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"is_toxic": score >= 0.17 # Threshold
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}
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# Example usage
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text = "ืื ื ืืืื ืืช ืืืื" # "I love everyone"
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print(predict(text))
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```
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## Training Info
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The model was trained using an optimized pipeline featuring:
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- **Gradient Accumulation:** Ensures stable training with larger effective batch sizes.
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- **Smart Text Cleaning:** Removes noise while preserving Hebrew, English, and important symbols (`@#$%*`).
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- **Dynamic Padding:** Uses efficient token lengths based on data distribution.
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## License
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CC-BY-SA-4.0
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## Citation
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```bibtex
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@misc{opencensor-h1-mini,
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title = {OpenCensor-H1-Mini: Hebrew Profanity Detection Model},
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author = {LikoKIko},
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year = {2025},
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url = {https://huggingface.co/LikoKIko/OpenCensor-H1-Mini}
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}
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```
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