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README.md

@@ -4,6 +4,8 @@ tags:
 - security
 - cyber-security
 - CWE
+- vulnerability-classification
+- cve
 license: apache-2.0
 datasets:
 - zefang-liu/cve-and-cwe-mapping-dataset
@@ -11,44 +13,52 @@ language:
 - en
 metrics:
 - accuracy
+- f1
 base_model:
 - distilbert/distilbert-base-uncased
 pipeline_tag: text-classification
+model-index:
+- name: cwe-predictor
+  results:
+  - task:
+      type: text-classification
+      name: CWE Classification
+    metrics:
+    - type: accuracy
+      value: 0.727207
+      name: Validation Accuracy
+    - type: f1
+      value: 0.251264
+      name: Macro F1 Score
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-This model is designed to allow you to predict a single CWE given your description of a vulnerability.
+# CWE Predictor - Vulnerability Classification Model
 
+This model classifies vulnerability descriptions into Common Weakness Enumeration (CWE) categories. It's designed to help security professionals and developers quickly identify the type of vulnerability based on textual descriptions.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-The model takes in text and predicts a single CWE. On it's last training run it saw the following:
-
-- Training Loss: 1.158700
-- Validation Loss: 1.199677
-- Accuracy: 0.71136
-- F1: 0.229855
+This is a fine-tuned DistilBERT model that predicts CWE (Common Weakness Enumeration) categories from vulnerability descriptions. The model was trained on a comprehensive dataset of CVE descriptions mapped to their corresponding CWE identifiers.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**Key Features:**
+- Classifies vulnerabilities into 232 distinct CWE categories
+- Trained on 111,640 vulnerability descriptions
+- Achieves 72.72% accuracy on validation set
+- Macro F1 score of 0.251 demonstrating balanced performance across classes
+- Lightweight and fast inference using DistilBERT architecture
 
 - **Developed by:** [mulliken](https://huggingface.co/mulliken)
-- **Model type:** BERT
+- **Model type:** DistilBERT (Transformer-based classifier)
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
 - **Finetuned from model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Hugging Face Model:** [mulliken/cwe-predictor](https://huggingface.co/mulliken/cwe-predictor)
+- **Dataset:** [CVE and CWE Mapping Dataset](https://huggingface.co/datasets/zefang-liu/cve-and-cwe-mapping-dataset)
 
 ## Uses
 
@@ -56,66 +66,140 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model can be used directly for:
+- **Vulnerability Triage:** Automatically classify security vulnerabilities reported in bug bounty programs or security audits
+- **Security Analysis:** Categorize CVE descriptions to understand vulnerability patterns
+- **Automated Security Reporting:** Generate CWE classifications for vulnerability reports
+- **Security Research:** Analyze trends in vulnerability types across codebases
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be integrated into:
+- Security scanning tools and SAST/DAST platforms
+- Vulnerability management systems
+- Security information and event management (SIEM) systems
+- DevSecOps pipelines for automated vulnerability classification
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model should NOT be used for:
+- Medical or safety-critical systems without additional validation
+- As the sole method for security assessment (should complement human expertise)
+- Classifying non-English vulnerability descriptions
+- Real-time security detection (model is designed for post-discovery classification)
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Known Limitations
+- **Class Imbalance:** Some CWE categories are underrepresented in the training data, which may lead to lower accuracy for rare vulnerability types
+- **Temporal Bias:** Model trained on historical CVE data may not recognize newer vulnerability patterns
+- **Language Limitation:** Only trained on English descriptions
+- **Context Loss:** Limited to 512 tokens, longer descriptions are truncated
 
-[More Information Needed]
+### Risks
+- False negatives could lead to unidentified security vulnerabilities
+- Should not replace human security expertise
+- May not generalize well to proprietary or domain-specific vulnerability descriptions
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Always use this model as a supplementary tool alongside human security expertise
+- Validate predictions for critical security decisions
+- Consider retraining or fine-tuning for domain-specific applications
+- Monitor model performance over time as new vulnerability types emerge
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("mulliken/cwe-predictor")
+tokenizer = AutoTokenizer.from_pretrained("mulliken/cwe-predictor")
+
+# Prediction function
+def predict_cwe(text: str) -> str:
+    encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        logits = model(**encoded).logits
+        pred_id = torch.argmax(logits, dim=-1).item()
+    return model.config.id2label[pred_id]
+
+# Example usage
+vuln_description = "Buffer overflow in the authentication module allows remote attackers to execute arbitrary code."
+cwe_prediction = predict_cwe(vuln_description)
+print(f"Predicted CWE: {cwe_prediction}")
+```
+
+### Example Predictions
 
-[More Information Needed]
+```python
+examples = [
+    "SQL injection vulnerability in login form allows attackers to bypass authentication",
+    "Cross-site scripting (XSS) vulnerability in comment section",
+    "Path traversal vulnerability allows reading arbitrary files",
+    "Integer overflow in image processing library causes memory corruption"
+]
+
+for desc in examples:
+    print(f"Description: {desc}")
+    print(f"Predicted CWE: {predict_cwe(desc)}\n")
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+The model was trained on the [CVE and CWE Mapping Dataset](https://huggingface.co/datasets/zefang-liu/cve-and-cwe-mapping-dataset), which contains:
+- CVE descriptions from the National Vulnerability Database (NVD)
+- Corresponding CWE classifications
+- Dataset size: 124,045 examples after filtering
+- Training set: 111,640 examples
+- Validation set: 12,405 examples
+- Number of CWE classes: 232 (after removing generic categories like "NVD-CWE-Other" and "NVD-CWE-noinfo")
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
+#### Preprocessing
 
-[More Information Needed]
+1. **Data Cleaning:**
+   - Removed entries with missing descriptions or CWE IDs
+   - Filtered out generic CWE categories ("NVD-CWE-Other", "NVD-CWE-noinfo")
+   - Removed CWE categories with only 1 example to ensure stratified splitting
 
+2. **Tokenization:**
+   - Used DistilBERT tokenizer with max_length=512
+   - Applied truncation for longer descriptions
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Learning rate:** 2e-5
+- **Batch size:** 2 per device with gradient accumulation of 8 (effective batch size: 16)
+- **Number of epochs:** 1
+- **Weight decay:** 0.01
+- **Optimizer:** AdamW
+- **Training regime:** fp32 with gradient checkpointing
+- **Evaluation strategy:** Every 1000 steps
 
-#### Speeds, Sizes, Times [optional]
+#### Training Performance
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Total training time:** ~78 minutes (4712 seconds) (per epoch)
+- **Training steps:** 13,956 
+- **Training samples per second:** 23.691
+- **Final training loss:** 1.134700
+- **Best validation loss:** 1.082806 (at step 6000)
+- **Model size:** ~268MB
 
 ## Evaluation
 
@@ -125,92 +209,102 @@ Use the code below to get started with the model.
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Validation set of 12,405 examples (10% stratified split from the training data)
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Accuracy:** Overall correctness of predictions
+- **Macro F1 Score:** Unweighted mean of F1 scores for each class (ensures balanced performance across all CWE types)
 
 ### Results
 
-[More Information Needed]
+| Step | Training Loss | Validation Loss | Accuracy | Macro F1 |
+|------|--------------|-----------------|----------|----------|
+| 1000 | 1.044600 | 1.252940 | 0.704716 | 0.220344 |
+| 2000 | 1.158700 | 1.188677 | 0.711326 | 0.229855 |
+| 3000 | 1.119900 | 1.159229 | 0.719226 | 0.235295 |
+| 4000 | 1.112600 | 1.119924 | 0.720193 | 0.242404 |
+| 5000 | 1.110300 | 1.111053 | 0.722934 | 0.244389 |
+| 6000 | 1.134700 | 1.082806 | 0.727207 | 0.251264 |
 
 #### Summary
 
+The model achieves 72.72% accuracy on the validation set with a macro F1 score of 0.251. The relatively lower F1 score reflects the challenge of classifying across 232 different CWE categories with varying representation in the dataset.
 
 
-## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
+## Model Examination
 
-[More Information Needed]
+The model uses standard DistilBERT attention mechanisms to process vulnerability descriptions. Key observations:
+- The model learns to identify security-related keywords and patterns
+- Attention weights typically focus on vulnerability-specific terms (e.g., "overflow", "injection", "traversal")
+- Performance varies by CWE category based on training data representation
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** Apple Silicon (M-series chip)
+- **Hours used:** ~1.3 hours
+- **Cloud Provider:** Local training (no cloud provider)
+- **Compute Region:** N/A (local)
+- **Carbon Emitted:** Minimal (Apple Silicon is energy efficient, ~15W TDP)
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- **Base Architecture:** DistilBERT (distilbert-base-uncased)
+- **Task:** Multi-class text classification
+- **Number of labels:** 232 CWE categories
+- **Objective:** Cross-entropy loss for sequence classification
+- **Architecture modifications:** Added classification head with 232 output classes
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Local machine with Apple Silicon processor
 
 #### Hardware
 
-[More Information Needed]
+- **Device:** Apple Silicon (MPS backend)
+- **Memory management:** PYTORCH_MPS_HIGH_WATERMARK_RATIO set to 0.0
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
+- **Framework:** PyTorch with Hugging Face Transformers
+- **Python version:** 3.x
+- **Key libraries:** transformers, torch, datasets, scikit-learn, pandas, numpy
 
-**APA:**
+## Citation
 
-[More Information Needed]
+If you use this model in your research, please cite:
 
-## Glossary [optional]
+```bibtex
+@misc{mulliken2024cwepredictcr,
+  author = {mulliken},
+  title = {CWE Predictor: A DistilBERT Model for Vulnerability Classification},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/mulliken/cwe-predictor}}
+}
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Glossary
 
-[More Information Needed]
+- **CWE (Common Weakness Enumeration):** A community-developed list of software and hardware weakness types
+- **CVE (Common Vulnerabilities and Exposures):** A list of publicly disclosed cybersecurity vulnerabilities
+- **NVD (National Vulnerability Database):** U.S. government repository of vulnerability management data
+- **Macro F1:** The unweighted mean of F1 scores calculated for each class independently
+- **SAST/DAST:** Static/Dynamic Application Security Testing
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+For questions, issues, or contributions, please visit the [Hugging Face model page](https://huggingface.co/mulliken/cwe-predictor).
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+- [mulliken](https://huggingface.co/mulliken)
 
 ## Model Card Contact
 
-[More Information Needed]
+Please use the Hugging Face model repository's discussion section for questions and feedback: [mulliken/cwe-predictor](https://huggingface.co/mulliken/cwe-predictor/discussions)