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Update README with corrected card and Denash org references

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  1. README.md +15 -5
README.md CHANGED
@@ -2,7 +2,7 @@
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  > HuggingFace-ready model card. This documents the custom Stage-2 classifier
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  > trained by `src/training/` and is the artifact to publish at
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- > `huggingface.co/cevud/codebert-vuln-classifier`.
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  ---
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@@ -10,7 +10,7 @@
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  ### Model Description
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- - **Model ID**: `cevud/codebert-vuln-classifier`
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  - **Model type**: Fine-tuned transformer for binary sequence classification
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  - **Base model**: [`microsoft/codebert-base`](https://huggingface.co/microsoft/codebert-base) (RoBERTa-based, ~125 M parameters)
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  - **Language**: Python (code)
@@ -82,7 +82,7 @@ pipeline. Its intended use case is:
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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- model_id = "cevud/codebert-vuln-classifier"
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  tokenizer = AutoTokenizer.from_pretrained(model_id)
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  model = AutoModelForSequenceClassification.from_pretrained(model_id)
@@ -443,7 +443,7 @@ pip install transformers torch
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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- model_id = "cevud/codebert-vuln-classifier"
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  tokenizer = AutoTokenizer.from_pretrained(model_id)
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  model = AutoModelForSequenceClassification.from_pretrained(model_id)
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  model.eval()
@@ -507,10 +507,20 @@ CEVuD Authors
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  title={CEVuD: Cost-Effective Vulnerability Detection via Gated Static-Neural Reasoning},
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  author={CEVuD Authors},
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  year={2026},
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- note={Model: cevud/codebert-vuln-classifier; Dataset: cevud/cevud-training-dataset}
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  }
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  ```
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  ## Model Card Contact
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  Open an issue on the CEVuD GitHub repository.
 
 
 
 
 
 
 
 
 
 
 
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  > HuggingFace-ready model card. This documents the custom Stage-2 classifier
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  > trained by `src/training/` and is the artifact to publish at
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+ > `huggingface.co/Denash/codebert-vuln-classifier`.
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  ---
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  ### Model Description
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+ - **Model ID**: `Denash/codebert-vuln-classifier`
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  - **Model type**: Fine-tuned transformer for binary sequence classification
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  - **Base model**: [`microsoft/codebert-base`](https://huggingface.co/microsoft/codebert-base) (RoBERTa-based, ~125 M parameters)
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  - **Language**: Python (code)
 
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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+ model_id = "Denash/codebert-vuln-classifier"
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  tokenizer = AutoTokenizer.from_pretrained(model_id)
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  model = AutoModelForSequenceClassification.from_pretrained(model_id)
 
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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+ model_id = "Denash/codebert-vuln-classifier"
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  tokenizer = AutoTokenizer.from_pretrained(model_id)
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  model = AutoModelForSequenceClassification.from_pretrained(model_id)
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  model.eval()
 
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  title={CEVuD: Cost-Effective Vulnerability Detection via Gated Static-Neural Reasoning},
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  author={CEVuD Authors},
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  year={2026},
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+ note={Model: Denash/codebert-vuln-classifier; Training Dataset: Denash/cevud-training-dataset; Pipeline Dataset: Denash/cevud-pipeline-dataset}
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  }
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  ```
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  ## Model Card Contact
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  Open an issue on the CEVuD GitHub repository.
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+
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+ ## Related Resources
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+
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+ | Resource | Link |
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+ |----------|------|
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+ | **Training Dataset (CVEfixes)** | [`Denash/cevud-training-dataset`](https://huggingface.co/datasets/Denash/cevud-training-dataset) |
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+ | **Pipeline Dataset (VUDENC)** | [`Denash/cevud-pipeline-dataset`](https://huggingface.co/datasets/Denash/cevud-pipeline-dataset) |
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+ | **Source Dataset (CVEfixes)** | [`hitoshura25/cvefixes`](https://huggingface.co/datasets/hitoshura25/cvefixes) |
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+ | **Source Dataset (VUDENC)** | [`DetectVul/Vudenc`](https://huggingface.co/datasets/DetectVul/Vudenc) |
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+ | **CEVuD GitHub** | https://github.com/Denash/CEVuD |