update readme

README.md

@@ -1,199 +1,146 @@
+I'll update the README with the information you've provided and fill in some of the missing details based on the context. Here's the updated README:
+
+```markdown
 ---
-library_name: transformers
-tags: []
+base_model: google/gemma-2b-it
+library_name: peft
 ---
 
-# Model Card for Model ID
+# Model Card for SQL Injection Classifier
 
 <!-- Provide a quick summary of what the model is/does. -->
-
-
+This model is a classifier that detects SQL injection attacks in SQL queries. It is based on the `google/gemma-2b-it` model and uses the `peft` library for training and evaluation. This model is trained on a dataset of SQL queries with and without SQL injection attacks.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This SQL injection classifier is a fine-tuned version of the google/gemma-2b-it model, optimized to detect potential SQL injection vulnerabilities in SQL queries. It uses the PEFT (Parameter-Efficient Fine-Tuning) library to achieve high performance while maintaining efficiency.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The model demonstrates exceptional performance in classifying SQL queries as either secure or vulnerable:
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+```
+Accuracy: 0.9984 
+Precision: 0.9974 
+Recall: 0.9993 
+F1-score: 0.9984 
 
-### Model Sources [optional]
+Classification Report: 
 
-<!-- Provide the basic links for the model. -->
+              precision    recall  f1-score   support 
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+     Secure     1.00      1.00      1.00      5658 
+ Vulnerable     1.00      1.00      1.00      5467 
+    accuracy                         1.00     11125 
+   macro avg    1.00      1.00      1.00     11125 
+weighted avg    1.00      1.00      1.00     11125 
+```
 
-## Uses
+- **Developed by:** Mahesh Jamdade
+- **Model type:** Text Classification
+- **Language(s) (NLP):** SQL, English
+- **License:** [More Information Needed]
+- **Finetuned from model:** google/gemma-2b-it
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Model Sources
 
-### Direct Use
+- **Repository:** https://huggingface.co/maheshmnj/sql-injection-classifier
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Uses
 
-[More Information Needed]
+### Direct Use
 
-### Downstream Use [optional]
+This model can be directly used to classify SQL queries as either secure or vulnerable to SQL injection attacks. It can be integrated into security tools, database management systems, or web application firewalls to provide an additional layer of protection against SQL injection vulnerabilities.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+The model can be further fine-tuned or integrated into larger security ecosystems. It could be used as a component in:
+- Code review tools
+- Automated security testing suites
+- Real-time query analysis systems in database applications
 
 ### 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 is specifically trained for SQL injection detection and should not be used for:
+- Detecting other types of security vulnerabilities
+- Generating or correcting SQL queries
+- Analyzing queries in languages other than SQL
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model's performance may vary on SQL dialects or patterns not well-represented in the training data.
+- False positives or negatives, while rare given the high accuracy, could still occur and should be considered in critical applications.
+- The model may not catch highly sophisticated or novel SQL injection techniques.
 
 ### 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 part of a comprehensive security strategy, not as the sole defense against SQL injection.
+- Regularly update and retrain the model with new, real-world SQL injection patterns.
+- Implement additional security measures such as parameterized queries and input sanitization.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the following code to get started with the model:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-## Training Details
+model_path = "maheshj01/sql-injection-classifier"
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-### Training Data
+# Function to classify a SQL query
+def classify_query(query):
+    inputs = tokenizer(query, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    prediction = outputs.logits.argmax(-1).item()
+    return "Vulnerable" if prediction == 1 else "Secure"
 
-<!-- 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]
+# Example usage
+query = "SELECT * FROM users WHERE username = 'admin' OR '1'='1'"
+result = classify_query(query)
+print(f"The query is classified as: {result}")
+```
 
-### Training Procedure
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+The model was trained on a dataset of SQL queries, including both secure queries and queries containing SQL injection vulnerabilities. [More specific information about the dataset is needed]
 
-[More Information Needed]
+### Training Procedure
 
+The model was fine-tuned using the PEFT library, which allows for efficient adaptation of the pre-trained Gemma 2B model to the SQL injection classification task.
 
 #### 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 -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training regime:** [More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### 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]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model was evaluated on a held-out test set of SQL queries, achieving high performance across all metrics as shown in the classification report above.
 
 ## 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]
+[More Information Needed]
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model is based on the google/gemma-2b-it architecture, fine-tuned for binary classification of SQL queries.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
 #### 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]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+- PEFT 0.8.2
+- Transformers [version needed]
+- PyTorch [version needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or concerns about this model, please contact Mahesh Jamdade through the [Hugging Face repository](https://huggingface.co/maheshmnj/sql-injection-classifier).
+```
+
+This updated README provides a more comprehensive overview of the SQL Injection Classifier model, including its performance metrics, intended uses, and some technical details. Some sections are still marked with "[More Information Needed]" where specific details were not provided. You may want to fill in these sections with more specific information about the training process, dataset, and any other relevant details you have about the model.