Upload README.md with huggingface_hub

README.md

@@ -1,199 +1,139 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+tags:
+- text-classification
+- cybersecurity
+- data-validation
+- form-validation
+language:
+- en
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## 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]
-
-### 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]
-
-[More Information Needed]
-
-
-#### 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]
-
-## 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]
-
-## 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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### 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]
-
-## Model Card Contact
-
-[More Information Needed]
+# Cybersecurity Data Validation Model
+
+## 🛡️ Overview
+This model validates user input data according to cybersecurity standards. It performs binary classification to determine if personal information fields meet security formatting requirements.
+
+## 🎯 Model Purpose
+- **Task**: Binary Text Classification (VALID/INVALID)
+- **Domain**: Cybersecurity & Data Validation
+- **Use Case**: Form validation, data quality checking, input sanitization
+
+## 📋 Validation Rules
+
+The model checks if input data follows these cybersecurity standards:
+
+- **firstName**: Must be proper case (First letter capital, rest lowercase)
+  - ✅ Valid: "John", "Alice", "Maria"
+  - ❌ Invalid: "john", "ALICE", "mArIa"
+
+- **address**: Each word should be properly capitalized
+  - ✅ Valid: "123 Main Street", "789 Pine Road"
+  - ❌ Invalid: "123 main street", "789 PINE ROAD"
+
+- **mobile**: Must be exactly 10 digits
+  - ✅ Valid: "9876543210"
+  - ❌ Invalid: "98765", "98765432109"
+
+- **pincode**: Must be exactly 6 digits
+  - ✅ Valid: "560001", "400001"
+  - ❌ Invalid: "560", "5600012"
+
+## 🚀 Usage
+
+### Basic Usage
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline("text-classification", model="abinashv29gmailcom/cybersec-validation-model-v1")
+
+# Test input
+text = "firstName: John, address: 123 Main Street, mobile: 9876543210, pincode: 560001"
+result = classifier(text)
+
+print(result)
+# Output: [{'label': 'VALID', 'score': 0.95}]
+```
+
+### Batch Processing
+```python
+# Multiple inputs
+inputs = [
+    "firstName: Alice, address: 789 Pine Road, mobile: 7654321098, pincode: 400001",
+    "firstName: bob, address: main street, mobile: 98765, pincode: 123"
+]
+
+results = classifier(inputs)
+for i, result in enumerate(results):
+    status = "✅ VALID" if result['label'] == 'VALID' else "❌ INVALID"
+    print(f"Input {i+1}: {status} (Confidence: {result['score']:.3f})")
+```
+
+### Integration Function
+```python
+def validate_user_data(firstname, address, mobile, pincode):
+    input_text = f"firstName: {firstname}, address: {address}, mobile: {mobile}, pincode: {pincode}"
+    result = classifier(input_text)[0]
+    
+    return {
+        'is_valid': result['label'] == 'VALID',
+        'confidence': result['score'],
+        'status': result['label'],
+        'input': input_text
+    }
+
+# Example usage
+validation_result = validate_user_data("John", "123 Main Street", "9876543210", "560001")
+print(validation_result)
+```
+
+## 📊 Examples
+
+### ✅ Valid Examples
+```
+Input: "firstName: John, address: 123 Main Street, mobile: 9876543210, pincode: 560001"
+Output: VALID (High Confidence)
+
+Input: "firstName: Alice, address: 789 Pine Road, mobile: 7654321098, pincode: 400001"
+Output: VALID (High Confidence)
+```
+
+### ❌ Invalid Examples
+```
+Input: "firstName: john, address: main street, mobile: 98765, pincode: 123"
+Output: INVALID (Multiple violations)
+
+Input: "firstName: MARY, address: APARTMENT 5B, mobile: 1234567890, pincode: 1234567"
+Output: INVALID (Formatting issues)
+```
+
+## 🔧 Technical Details
+- **Base Model**: distilbert-base-uncased
+- **Architecture**: DistilBERT for Sequence Classification
+- **Labels**: 2 classes (VALID, INVALID)
+- **Max Sequence Length**: 128 tokens
+- **Framework**: Transformers, PyTorch
+
+## 🎯 Intended Applications
+- **Web Form Validation**: Real-time validation of user registration forms
+- **Data Quality Assurance**: Batch processing of existing datasets
+- **API Integration**: RESTful services for validation endpoints
+- **Mobile Apps**: Client-side or server-side validation
+- **Compliance Checking**: Ensure data meets cybersecurity standards
+
+## ⚠️ Limitations
+- Designed for English language inputs
+- Specific to the defined validation rules
+- May require fine-tuning for domain-specific requirements
+- Performance may vary with inputs significantly different from training examples
+
+## 👨‍💻 Created By
+**Abinash V** - Cybersecurity Data Validation System
+
+## 📄 License
+Apache 2.0
+
+## 🔄 Version History
+- v1.0: Initial model with basic cybersecurity validation rules