Update model description with comprehensive details from GitHub research project

README.md

@@ -1,204 +1,269 @@
 ---
 library_name: peft
 base_model: microsoft/phi-2
+tags:
+- biomedical
+- summarization
+- lay-summary
+- healthcare
+- nlp
+- fine-tuned
+- lora
+- peft
+- elife
+- plos
+- medical-text
+language:
+- en
+license: mit
+metrics:
+- rouge
+- bertscore
+- readability
+datasets:
+- elife
+- plos
+pipeline_tag: text2text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-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
+# Phi-2 BioLaySum: Biomedical Lay Summarization Model 🏆
+
+## 📖 Model Overview
+
+**Phi-2 BioLaySum** is a **champion model** that emerged as the most efficient and highest-performing solution for generating lay summaries of biomedical articles. This model converts complex medical research into easily understandable summaries for the general public, significantly enhancing accessibility to scientific literature.
+
+**🥇 Key Achievement**: This model **outperformed** T5-Base, T5-Large, FlanT5-Base, BioGPT, and Falconsi-Medical_summarisation across all evaluation dimensions (relevance, readability, and factuality) while maintaining optimal computational efficiency.
+
+## 🎯 Model Purpose
+
+This model addresses the critical need to bridge the gap between complex biomedical research and public health literacy by:
+- Converting medical articles into patient-friendly summaries
+- Supporting healthcare communication between professionals and patients
+- Enhancing public access to biomedical research findings
+- Enabling better-informed health decisions by the general public
+
+## 🏗️ Model Architecture
+
+- **Base Model**: microsoft/phi-2
+- **Fine-tuning Technique**: LoRA (Low-Rank Adaptation) + PEFT (Parameter Efficient Fine-tuning)
+- **Model Type**: Text-to-Text Generation (Summarization)
+- **Language**: English
+- **Domain**: Biomedical/Healthcare
+
+## 📊 Performance Highlights
+
+### Why Phi-2 is the Champion Model:
+- ✅ **Superior Performance**: Best scores across relevance, readability, and factuality metrics
+- ✅ **Resource Efficiency**: Optimal performance-to-resource ratio
+- ✅ **Compact Size**: Most efficient in terms of model size and computational requirements
+- ✅ **Cost-Effective**: Best balance of quality and computational cost
+
+### Evaluation Results:
+- **Relevance**: Measured using ROUGE (1, 2, L) and BERTScore
+- **Readability**: Assessed via Flesch-Kincaid Grade Level (FKGL) and Dale-Chall Readability Score (DCRS)
+- **Factuality**: Verified using BARTScore and factual consistency checks
+
+## 🚀 Quick Start
+
+### Loading the Model
+
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the base model and tokenizer
+base_model_name = "microsoft/phi-2"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Load the fine-tuned adapter
+model = PeftModel.from_pretrained(model, "sank29mane/phi-2-biolaysum")
+
+# Set padding token
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+```
+
+### Generating Lay Summaries
+
+```python
+def generate_lay_summary(medical_text, max_length=150):
+    # Prepare input
+    prompt = f"Summarize the following medical text for a general audience: {medical_text}"
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+    
+    # Generate summary
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    # Decode and return
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary.split(":")[-1].strip()  # Extract generated part
+
+# Example usage
+medical_text = """
+The study investigated the efficacy of novel therapeutic interventions 
+in cardiovascular disease management through randomized controlled trials...
+"""
+
+lay_summary = generate_lay_summary(medical_text)
+print(f"Lay Summary: {lay_summary}")
+```
+
+## 📚 Training Details
 
 ### Training Data
+- **eLife Dataset**: Open-access biomedical research articles with lay summaries
+- **PLOS Dataset**: Public Library of Science biomedical publications
+- **Data Processing**: Advanced preprocessing for optimal model performance
+
+### Training Configuration
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) with PEFT
+- **Base Model**: microsoft/phi-2
+- **Training Framework**: PyTorch + Hugging Face Transformers
+- **Optimization**: Parameter-efficient approach reducing computational requirements
+
+### Training Advantages
+- **Efficiency**: LoRA reduces trainable parameters while maintaining performance
+- **Resource-Friendly**: PEFT enables high-quality fine-tuning with limited resources
+- **Stability**: Advanced techniques ensure robust model behavior
+
+## 📈 Comparative Analysis
+
+### Models Compared:
+1. **T5-Base** - Text-to-Text Transfer Transformer (Base)
+2. **T5-Large** - Text-to-Text Transfer Transformer (Large)
+3. **FlanT5-Base** - Instruction-tuned T5 model
+4. **BioGPT** - Biomedical domain-specific GPT
+5. **Phi-2** - Microsoft's efficient language model (**Winner**)
+6. **Falconsi-Medical_summarisation** - Specialized medical summarization model
+
+### Key Findings:
+- **Phi-2 outperformed all competitors** in comprehensive evaluation
+- **Domain-specific models** (BioGPT, Falconsi) showed advantages over general T5 models
+- **Parameter efficiency** of Phi-2 provided superior cost-effectiveness
+- **Smaller models** can achieve better performance with proper fine-tuning
+
+## 🎯 Use Cases
+
+### Healthcare Applications:
+- **Patient Education**: Convert research findings into understandable format
+- **Medical Communication**: Support doctor-patient conversations
+- **Health Journalism**: Assist science writers and health reporters
+- **Educational Materials**: Create teaching resources for health education
+- **Policy Support**: Provide accessible summaries for health policy decisions
+
+### Target Audiences:
+- Healthcare professionals seeking patient communication tools
+- Patients and families researching medical conditions
+- Health educators and trainers
+- Medical journalists and science communicators
+- Public health policy makers
+
+## ⚡ Performance Metrics
+
+### Evaluation Framework:
+- **ROUGE Scores**: Overlap-based relevance assessment
+- **BERTScore**: Semantic similarity evaluation
+- **Readability Metrics**: FKGL and DCRS for accessibility
+- **Factual Consistency**: BARTScore for accuracy verification
+
+### Resource Efficiency:
+- **Model Size**: Compact and deployment-friendly
+- **Inference Speed**: Fast generation suitable for real-time applications
+- **Memory Usage**: Optimized for various computational environments
+- **Cost Effectiveness**: Best performance per computational dollar
+
+## 🔧 Technical Specifications
+
+### Model Details:
+- **Architecture**: Transformer-based with LoRA adaptation
+- **Parameters**: Base Phi-2 + efficient LoRA adapters
+- **Precision**: Mixed precision training for efficiency
+- **Framework**: PyTorch with Hugging Face ecosystem
+
+### System Requirements:
+- **Minimum GPU**: 4GB VRAM for inference
+- **Recommended**: 8GB+ VRAM for optimal performance
+- **CPU**: Compatible with CPU inference (slower)
+- **Dependencies**: transformers, peft, torch
+
+## 📖 Research Impact
+
+This model contributes to:
+- **Democratizing Medical Knowledge**: Making research accessible to all
+- **Advancing Healthcare NLP**: Pushing boundaries of medical text processing
+- **Resource-Efficient AI**: Demonstrating effective use of LoRA and PEFT
+- **Evaluation Methodology**: Comprehensive framework for summarization assessment
+
+## 📄 License & Citation
+
+### License
+This model is released under the **MIT License**, promoting open research and development.
+
+### Citation
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{mane2024phi2biolaysum,
+  title={Phi-2 BioLaySum: Resource-Efficient Biomedical Lay Summarization using LoRA and PEFT},
+  author={Mane, Sanket},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/sank29mane/phi-2-biolaysum}
+}
+```
+
+## 🔗 Related Resources
+
+- **GitHub Repository**: [lays-bio-summery](https://github.com/sank29mane/lays-bio-summery) - Complete training code and evaluation
+- **Base Model**: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
+- **Research Paper**: [Detailed methodology and results](https://github.com/sank29mane/lays-bio-summery)
+
+## 👨‍💻 Author
 
-<!-- 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. -->
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-[More Information Needed]
-
-## Evaluation
+**Sanket Mane** - [@sank29mane](https://github.com/sank29mane)  
+*Researcher in Biomedical NLP and Efficient Language Models*
+
+## 📞 Contact & Support
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+- **GitHub Issues**: [Create an issue](https://github.com/sank29mane/lays-bio-summery/issues)
+- **Model Issues**: Use the Community tab above
+- **Research Collaborations**: Through GitHub profile
+
+## 🚨 Limitations & Considerations
+
+### Current Limitations:
+- **Language**: Currently optimized for English biomedical text
+- **Domain**: Focused on general biomedical research (not clinical notes)
+- **Length**: Optimized for article-length inputs, may vary with very short/long texts
 
-### Testing Data, Factors & Metrics
+### Recommended Use:
+- Use for biomedical research article summarization
+- Validate outputs for critical healthcare decisions
+- Consider human review for patient-facing applications
 
-#### Testing Data
+## 🔄 Model Updates
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-
-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
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-
-## 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).
-
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+- **v1.0**: Initial release with LoRA+PEFT fine-tuning
+- **Future**: Planned improvements for multi-language support and clinical text adaptation
 
+---
 
-### Framework versions
+### Framework Versions
+- **PEFT**: 0.7.2.dev0
+- **Transformers**: Compatible with latest versions
+- **PyTorch**: 1.12+
 
-- PEFT 0.7.2.dev0
+⭐ **Star this model if you find it useful for your biomedical NLP research!** ⭐