Update README.md

README.md

@@ -21,95 +21,86 @@ context: The train was unusually empty as Aarav boarded it late one evening, the
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-
-# Model Card: Humaneyes Text Paraphraser
-
+# Humaneyes 
 ## Model Description
 
-Humaneyes is an advanced text paraphrasing model built using the Pegasus transformer architecture. The model is designed to generate high-quality, contextually-aware paraphrases while preserving the original text's paragraph structure and semantic meaning.
+Humaneyes is an advanced text transformation model designed to convert AI-generated text into more human-like content and provide robust defense against AI content detection trackers. The model leverages sophisticated natural language processing techniques to humanize machine-generated text, making it indistinguishable from human-written content.
 
 ### Model Details
 - **Developed by:** Eemansleepdeprived
-- **Model type:** Text-to-text generation (Paraphrasing)
+- **Model type:** AI-to-Human Text Transformation
+- **Primary Functionality:** 
+  - AI-generated text humanization
+  - AI tracker defense
 - **Language(s):** English
-- **Base model:** Google Pegasus Large
-- **Input format:** Plain text
-- **Output format:** Paraphrased text
-
-## Intended Use
-
-### Primary Use Cases
-- Academic writing: Helping researchers and students rephrase text
-- Content creation: Assisting writers in generating alternative text variations
-- Language learning: Providing examples of different ways to express ideas
-
-### Potential Limitations
-- May not perfectly preserve highly technical or domain-specific language
-- Performance can vary depending on input text complexity
-- Not recommended for professional legal or medical document translation
-
-## Performance and Evaluation
-
-### Key Features
-- Preserves paragraph structure
-- Maintains semantic meaning
-- Handles various text lengths and complexities
-- Supports sentence-level paraphrasing
-
-### Evaluation Metrics
-- Semantic similarity
-- Readability
-- Grammatical correctness
-
-
-## Training Data
-
-### Training Methodology
-- Base model: Trained on a diverse corpus of English text
-- Fine-tuning: Specific details of paraphrasing fine-tuning
-
-### Dataset Characteristics
-- Diverse text sources
-- Multiple domains and writing styles
-
-## Ethical Considerations
-
-### Bias and Fairness
-- Regular assessments for potential biases in paraphrasing
-- Commitment to continuous improvement of model fairness
-
-### Usage Guidelines
-- Intended for supportive, creative purposes
-- Not designed to replace original authorship
-- Encourage proper attribution and original thinking
-
-## Limitations and Potential Biases
-
-- May occasionally produce text that diverges significantly from the original
-- Could introduce subtle semantic shifts
-- Performance may vary across different text domains
-
-## How to Use
-
-### Example Usage
-
+- **Base Architecture:** Pegasus Transformer
+- **Input format:** AI-generated text
+- **Output format:** Humanized, natural-sounding text
+
+## Key Capabilities
+- Transforms AI-generated text to sound more natural and human-like
+- Defeats AI content detection algorithms
+- Preserves original semantic meaning
+- Maintains coherent paragraph structure
+- Introduces human-like linguistic variations
+
+## Intended Use Cases
+- Academic writing assistance
+- Content creation and disguising AI-generated content
+- Protecting writers from AI content detection systems
+- Enhancing AI-generated text for more authentic communication
+
+### Ethical Considerations
+- Intended for creative and protective purposes
+- Users should respect academic and professional integrity
+- Encourages responsible use of AI-generated content
+- Not designed to facilitate academic dishonesty
+
+## Technical Approach
+### Humanization Strategies
+- Natural language variation
+- Contextual rephrasing
+- Introducing human-like imperfections
+- Semantic preservation
+- Stylistic diversification
+
+### Anti-Detection Techniques
+- Defeating AI content trackers
+- Randomizing linguistic patterns
+- Simulating human writing nuances
+- Breaking predictable AI generation signatures
+
+## Performance Characteristics
+- High semantic similarity to original text
+- Reduced AI detection probability
+- Contextually appropriate transformations
+- Minimal loss of original meaning
+
+## Limitations
+- Performance may vary based on input text complexity
+- Not guaranteed to bypass all AI detection systems
+- Potential subtle semantic shifts
+- Effectiveness depends on input text characteristics
+
+## Usage Example
 ```python
 from transformers import PegasusTokenizer, PegasusForConditionalGeneration
 
 tokenizer = PegasusTokenizer.from_pretrained('Eemansleepdeprived/Humaneyes')
 model = PegasusForConditionalGeneration.from_pretrained('Eemansleepdeprived/Humaneyes')
 
-input_text = "Your original text goes here."
-inputs = tokenizer(input_text, return_tensors="pt")
+ai_generated_text = "Your AI-generated text goes here."
+inputs = tokenizer(ai_generated_text, return_tensors="pt")
 outputs = model.generate(**inputs)
-paraphrased_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+humanized_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 ```
 
 ## Contact and Collaboration
-
-For questions, feedback, or collaboration opportunities, please contact Eemansleepdeprived at link [email protected].
-
+For inquiries, feedback, or collaboration opportunities, contact:
+- Email: [email protected]
 
 ## License
+Released under the MIT License
 
-This model is released under the MIT License.
+## Disclaimer
+Users are responsible for ethical use of the Humaneyes Text Humanizer. Respect academic and professional guidelines.