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