Publish code_detection model and artifacts

README.md

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+---
+language:
+- code
+library_name: optimum
+pipeline_tag: text-classification
+tags:
+- code-detection
+- safety
+- onnx
+- hikmaai
+license: apache-2.0
+---
+
+# hikmaai-codebert-base-code-detection
+
+A binary classifier that detects whether the input contains source code,
+fine-tuned from
+[microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base)
+by [HikmaAI](https://huggingface.co/HikmaAI).
+
+## Model Description
+
+- **Task**: Binary classification (safe=0, threat=1, where "threat" = code detected)
+- **Base model**: `microsoft/codebert-base`
+- **Export formats**: ONNX FP32 + INT8 dynamic quantization
+
+## Performance
+
+See `model_card.json` for detailed metrics.
+
+Optimized threshold: **0.9950** (val recall: 0.9984)
+
+## Usage (ONNX)
+
+```python
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer
+
+model = ORTModelForSequenceClassification.from_pretrained(
+    "HikmaAI/hikmaai-codebert-base-code-detection",
+    subfolder="onnx/int8",
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "HikmaAI/hikmaai-codebert-base-code-detection",
+    subfolder="tokenizer",
+)
+
+inputs = tokenizer("def hello():\n    print('hi')", return_tensors="pt")
+outputs = model(**inputs)
+# outputs.logits -> [safe_score, threat_score]
+```
+
+## Training
+
+- Epochs: 5
+- Learning rate: 2e-05
+- Batch size: 16
+- Class weights: [1.0, 2.0]
+
+## License
+
+Apache-2.0
+
+## Citation
+
+```bibtex
+@misc{hikmaai-code_detection-2026,
+  title={hikmaai-codebert-base-code-detection},
+  author={HikmaAI},
+  year={2026},
+  publisher={HuggingFace},
+  url={https://huggingface.co/HikmaAI/hikmaai-codebert-base-code-detection}
+}
+```

model_card.json

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+{
+  "classifier_name": "code_detection",
+  "version": "1.0.0",
+  "task_type": "sequence_classification",
+  "base_model": "microsoft/codebert-base",
+  "metrics": {},
+  "dataset_hash": "",
+  "created_at": "2026-05-06T14:01:09.044946Z",
+  "export_format": "onnx",
+  "quantization": "int8_dynamic"
+}

onnx/fp32/config.json

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+{
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+    "RobertaForSequenceClassification"
+  ],
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+  "dtype": "float32",
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+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.6",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

onnx/fp32/model.onnx

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onnx/fp32/special_tokens_map.json

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onnx/fp32/tokenizer_config.json

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onnx/int8/config.json

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+}

onnx/int8/model_quantized.onnx

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onnx/int8/ort_config.json

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onnx/int8/special_tokens_map.json

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onnx/int8/tokenizer_config.json

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threshold.json

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+}

tokenizer/special_tokens_map.json

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