init

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,114 @@
 ---
+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 = 0.49*
+
+### Training Graphs
+
+| Validation F1 | Threshold Analysis |
+| :---: | :---: |
+| ![Validation F1](valf1perepoch.png) | ![Thresholds](thresholdsperepoch.png) |
+
+![Final Test Metrics](testmetrics.png)
+
+## 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 >= 0.49  # Threshold
+    }
+
+# Example usage
+text = "אני אוהב את כולם" # "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}
+}
+```

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "onlplab/alephbert-base",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 52000
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:422b637c5d96c9f944e7e3b1d0cef7aafb396156b918dfa13dd95b9ed8b5fbf6
+size 503932924

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}