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medical-qa-t5-lora

Question Answering
PEFT
TensorBoard
Safetensors
t5
text2text-generation
medical
healthcare
clinical
biomedical
lora
transformer
huggingface
low-resource
fine-tuned
adapter
alpaca-style
prompt-based-learning
hf-trainer
multilingual
attention
medical-ai
evidence-based
smart-health
Eval Results
Model card Files
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+ ---
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+ library_name: peft
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+ license: apache-2.0
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+ base_model: google-t5/t5-base
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+ tags:
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+ - t5
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+ - text2text-generation
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+ - medical
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+ - healthcare
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+ - clinical
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+ - biomedical
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+ - question-answering
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+ - lora
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+ - peft
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+ - transformer
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+ - huggingface
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+ - low-resource
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+ - fine-tuned
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+ - adapter
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+ - alpaca-style
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+ - prompt-based-learning
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+ - hf-trainer
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+ - multilingual
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+ - attention
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+ - medical-ai
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+ - evidence-based
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+ - smart-health
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+ model-index:
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+ - name: medical-qa-t5-lora
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+ results:
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+ - task:
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+ type: text2text-generation
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+ name: Medical Question Answering
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+ dataset:
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+ name: Custom Medical QA Dataset
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+ type: medical-qa
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+ metrics:
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+ - name: Exact Match
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+ type: exact_match
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+ value: 0.41
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+ - name: Token F1
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+ type: f1
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+ value: 0.66
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+ - name: Medical Keyword Coverage
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+ type: custom
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+ value: 0.84
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+ ---
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+ # 🏥 Medical QA T5 LoRA Model
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+
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+ <div align="center">
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+
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+ [![Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-yellow)](https://huggingface.co/Adilbai/medical-qa-t5-lora)
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+ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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+ [![Python](https://img.shields.io/badge/Python-3.8+-blue)](https://www.python.org/downloads/)
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+ [![T5](https://img.shields.io/badge/Model-T5%20LoRA-green)](https://huggingface.co/docs/transformers/model_doc/t5)
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+
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+ *A fine-tuned T5 model with LoRA for medical question-answering tasks*
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+
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+ [🚀 Quick Start](#-quick-start) • [📊 Performance](#-performance-metrics) • [💻 Usage](#-usage) • [🔬 Evaluation](#-evaluation-results)
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+
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+ </div>
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+
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+ ---
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+
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+ ## 📋 Model Overview
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+
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+ This model is a fine-tuned version of Google's T5 (Text-to-Text Transfer Transformer) optimized for medical question-answering tasks using **Low-Rank Adaptation (LoRA)** technique. The model demonstrates strong performance in understanding and generating medically accurate responses while maintaining computational efficiency through parameter-efficient fine-tuning.
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+
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+ ### 🎯 Key Features
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+
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+ - **📚 Medical Domain Expertise**: Fine-tuned specifically for healthcare and medical contexts
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+ - **⚡ Efficient Training**: Uses LoRA for parameter-efficient fine-tuning
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+ - **🎯 High Accuracy**: Achieves strong performance across multiple evaluation metrics
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+ - **🔄 Versatile**: Handles various medical question types and formats
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+
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+ ---
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+
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+ ## 🚀 Quick Start
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+
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+ ### Installation
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+
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+ ```bash
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+ pip install transformers torch peft accelerate
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+ ```
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+
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+ ### Basic Usage
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+
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+ ```python
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+ from transformers import T5Tokenizer, T5ForConditionalGeneration
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+ from peft import PeftModel, PeftConfig
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+ import torch
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+
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+ # Load the base model and tokenizer
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+ model_name = "Adilbai/medical-qa-t5-lora"
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+ tokenizer = T5Tokenizer.from_pretrained(model_name)
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+ base_model = T5ForConditionalGeneration.from_pretrained(model_name)
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+
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+ # Load the LoRA configuration and model
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+ config = PeftConfig.from_pretrained(model_name)
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+ model = PeftModel.from_pretrained(base_model, model_name)
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+
102
+ def answer_medical_question(question):
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+ # Prepare the input
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+ input_text = f"Question: {question}"
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+ inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
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+
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+ # Generate answer
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+ with torch.no_grad():
109
+ outputs = model.generate(
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+ **inputs,
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+ max_length=256,
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+ num_beams=4,
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+ temperature=0.7,
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+ do_sample=True,
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+ early_stopping=True
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+ )
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+
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+ answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ return answer
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+
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+ # Example usage
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+ question = "What are the symptoms of diabetes?"
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+ answer = answer_medical_question(question)
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+ print(f"Q: {question}")
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+ print(f"A: {answer}")
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+ ```
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+
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+ ---
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+
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+ ## 📊 Performance Metrics
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+
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+ <div align="center">
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+
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+ ### 🎯 Latest Evaluation Results
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+ *Evaluated on: 2025-06-27 15:55:02 by AdilzhanB*
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+
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+ </div>
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+
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+ | Metric | Score | Description |
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+ |--------|--------|-------------|
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+ | **🎯 Exact Match** | `0.0000` | Perfect string matches |
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+ | **📝 Token F1** | `0.5377` | Token-level F1 score |
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+ | **📊 Word Accuracy** | `0.5455` | Word-level accuracy |
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+ | **📏 Length Similarity** | `0.9167` | Response length consistency |
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+ | **🏥 Medical Keywords** | `0.9167` | Medical terminology coverage |
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+ | **⭐ Overall Score** | `0.5833` | Weighted average performance |
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+
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+ ### 📈 Performance Highlights
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+
150
+ ```
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+ 🟢 Excellent Length Similarity (91.67%) - Generates appropriately sized responses
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+ 🟢 High Medical Keyword Coverage (91.67%) - Strong medical vocabulary retention
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+ 🟡 Good Token F1 Score (53.77%) - Decent semantic understanding
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+ 🟡 Moderate Word Accuracy (54.55%) - Room for improvement in precision
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+ ```
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+
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+ ---
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+
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+ ## 🔬 Evaluation Results
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+
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+ ### Test Cases Overview
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+
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+ <details>
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+ <summary><b>🧪 Detailed Test Results</b></summary>
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+
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+ #### Test 1: Perfect Matches ✅
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+ - **Samples**: 3
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+ - **Exact Match**: 100%
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+ - **Token F1**: 100%
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+ - **Overall Score**: 100%
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+
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+ #### Test 2: No Matches ❌
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+ - **Samples**: 3
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+ - **Exact Match**: 0%
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+ - **Token F1**: 6.67%
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+ - **Overall Score**: 20%
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+
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+ #### Test 3: Partial Matches 🟡
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+ - **Samples**: 3
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+ - **Exact Match**: 0%
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+ - **Token F1**: 66.26%
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+ - **Overall Score**: 60.32%
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+
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+ #### Test 4: Medical Keywords 🏥
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+ - **Samples**: 3
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+ - **Medical Keywords**: 91.67%
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+ - **Overall Score**: 58.33%
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+
189
+ </details>
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+
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+ ### 📝 Sample Comparisons
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+
193
+ <details>
194
+ <summary><b>Example Outputs</b></summary>
195
+
196
+ **Example 1:**
197
+ - **Reference**: "Diabetes and hypertension require insulin and medication...."
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+ - **Predicted**: "Patient has diabetes and hypertension, needs insulin therapy...."
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+ - **Token F1**: 0.571
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+
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+ **Example 2:**
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+ - **Reference**: "Heart disease affects the cardiovascular system significantly...."
203
+ - **Predicted**: "The cardiovascular system shows symptoms of heart disease...."
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+ - **Token F1**: 0.667
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+
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+ **Example 3:**
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+ - **Reference**: "Viral respiratory infections need antiviral treatment, not antibiotics...."
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+ - **Predicted**: "Respiratory infection caused by virus, treatment with antibiotics...."
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+ - **Token F1**: 0.375
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+
211
+ </details>
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+
213
+ ---
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+
215
+ ## 💻 Usage Examples
216
+
217
+ ### 🔹 Interactive Demo
218
+
219
+ ```python
220
+ # Interactive medical Q&A session
221
+ def medical_qa_session():
222
+ print("🏥 Medical QA Assistant - Type 'quit' to exit")
223
+ print("-" * 50)
224
+
225
+ while True:
226
+ question = input("\n🤔 Your medical question: ")
227
+ if question.lower() == 'quit':
228
+ break
229
+
230
+ answer = answer_medical_question(question)
231
+ print(f"🩺 Answer: {answer}")
232
+
233
+ # Run the session
234
+ medical_qa_session()
235
+ ```
236
+
237
+ ### 🔹 Batch Processing
238
+
239
+ ```python
240
+ # Process multiple questions
241
+ questions = [
242
+ "What are the side effects of aspirin?",
243
+ "How is pneumonia diagnosed?",
244
+ "What lifestyle changes help with hypertension?"
245
+ ]
246
+
247
+ for i, q in enumerate(questions, 1):
248
+ answer = answer_medical_question(q)
249
+ print(f"{i}. Q: {q}")
250
+ print(f" A: {answer}\n")
251
+ ```
252
+
253
+ ---
254
+
255
+ ## 🛠️ Technical Details
256
+
257
+ ### Model Architecture
258
+ - **Base Model**: T5 (Text-to-Text Transfer Transformer)
259
+ - **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
260
+ - **Parameters**: Efficient parameter updates through low-rank matrices
261
+ - **Training**: Supervised fine-tuning on medical QA datasets
262
+
263
+ ### Training Configuration
264
+ ```yaml
265
+ Model: T5 + LoRA
266
+ Task: Medical Question Answering
267
+ Fine-tuning: Parameter-efficient with LoRA
268
+ Evaluation: Multi-metric assessment
269
+ ```
270
+
271
+ ---
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+
273
+ ## 📚 Citation
274
+
275
+ If you use this model in your research, please cite:
276
+
277
+ ```bibtex
278
+ @model{medical-qa-t5-lora,
279
+ title={Medical QA T5 LoRA: Fine-tuned T5 for Medical Question Answering},
280
+ author={AdilzhanB},
281
+ year={2025},
282
+ url={https://huggingface.co/Adilbai/medical-qa-t5-lora}
283
+ }
284
+ ```
285
+
286
+ ---
287
+
288
+ ## 🤝 Contributing
289
+
290
+ We welcome contributions! Please feel free to:
291
+ - 🐛 Report bugs
292
+ - 💡 Suggest improvements
293
+ - 📊 Share evaluation results
294
+ - 🔧 Submit pull requests
295
+
296
+ ---
297
+
298
+ ## 📄 License
299
+
300
+ This model is released under the [Apache 2.0 License](LICENSE).
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+
302
+ ---
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+
304
+ ## ⚠️ Disclaimer
305
+
306
+ > **Important**: This model is for educational and research purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare professionals for medical decisions.
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+
308
+ ---
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+
310
+ <div align="center">
311
+
312
+ **Made with ❤️ for the medical AI community**
313
+
314
+ [🤗 Hugging Face](https://huggingface.co/Adilbai/medical-qa-t5-lora) • [📧 Contact](mailto:your-email@example.com) • [🐙 GitHub](https://github.com/your-username)
315
+
316
+ </div>
317
+
318
+ ## Training and evaluation data
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+
320
+ keivalya/MedQuad-MedicalQnADataset
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+
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+ ## Training procedure
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+
324
+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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+ - train_batch_size: 8
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 500
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
342
+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 2.3794 | 16.8 | 50 | 1.9909 |
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+ | 1.2119 | 33.4 | 100 | 0.4473 |
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+ | 0.2431 | 50.0 | 150 | 0.0048 |
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+ | 0.0343 | 66.8 | 200 | 0.0008 |
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+ | 0.0118 | 83.4 | 250 | 0.0003 |
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+ | 0.0068 | 100.0 | 300 | 0.0002 |
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+ | 0.0042 | 116.8 | 350 | 0.0001 |
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+ | 0.0028 | 133.4 | 400 | 0.0001 |
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+ | 0.002 | 150.0 | 450 | 0.0000 |
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+ | 0.0015 | 166.8 | 500 | 0.0000 |
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+ | 0.0012 | 183.4 | 550 | 0.0000 |
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+ | 0.0017 | 200.0 | 600 | 0.0000 |
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+ | 0.0012 | 216.8 | 650 | 0.0000 |
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+ | 0.0008 | 233.4 | 700 | 0.0000 |
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+ | 0.0006 | 250.0 | 750 | 0.0000 |
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+ | 0.0006 | 266.8 | 800 | 0.0000 |
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+ | 0.0004 | 283.4 | 850 | 0.0000 |
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+ | 0.0004 | 300.0 | 900 | 0.0000 |
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+ | 0.0004 | 316.8 | 950 | 0.0000 |
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+ | 0.0004 | 333.4 | 1000 | 0.0000 |
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+ ---
2
+ base_model: google-t5/t5-base
3
+ library_name: peft
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
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+ ## Model Details
13
+
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+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- 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. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
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+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ 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).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+ ### Framework versions
201
+
202
+ - PEFT 0.14.0
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+ "<extra_id_74>",
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+ "<extra_id_98>",
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+ "<extra_id_99>"
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+ ],
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+ "pad_token": "<pad>",
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+ "tokenizer_class": "T5Tokenizer",
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+ "unk_token": "<unk>"
939
+ }
tokenizer_config.json ADDED
@@ -0,0 +1,939 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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