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--- |
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language: en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- gpt2 |
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- text-generation |
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- medicine |
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- india |
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- pharmaceutical |
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- question-answering |
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base_model: gpt2 |
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--- |
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# Mayank-AI: Medical AI Assistant Model |
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[](https://huggingface.co/Mayank-22/Mayank-AI) |
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[](https://opensource.org/licenses/Apache-2.0) |
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[](https://huggingface.co/Mayank-22/Mayank-AI) |
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## 📋 Model Overview |
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Mayank-AI is a specialized artificial intelligence model designed for Indian pharmaceutical and medical applications, trained on comprehensive Indian medicines datasets. This model leverages supervised learning techniques built on GPT-2 transformer architecture to provide accurate and contextually relevant information about Indian medicines, their compounds, uses, and related medical information. |
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## 🔍 Model Details |
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### Model Description |
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- **Developed by:** Mayank Malviya |
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- **Model Type:** GPT-2 based Transformer for Indian Medical/Pharmaceutical Applications |
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- **Language(s):** English (with Indian medical terminology and drug names) |
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- **License:** Apache-2.0 |
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- **Domain:** Indian Pharmaceuticals & Medicine Information |
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- **Primary Use:** Indian medicine information, drug compound analysis, symptom mapping, prescription guidance |
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### Key Features |
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- ✅ Indian medicines database knowledge |
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- ✅ Drug compound information and analysis |
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- ✅ Symptom-to-medicine mapping |
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- ✅ Prescription guidance and recommendations |
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- ✅ Disease diagnosis assistance |
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- ✅ Indian pharmaceutical market insights |
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- ✅ Medicine availability and alternatives |
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## 🚀 Quick Start |
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### Installation |
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```bash |
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pip install transformers torch |
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``` |
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### Basic Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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# Load model and tokenizer |
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model_name = "Mayank-22/Mayank-AI" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Example queries for Indian medicines |
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query1 = "What is the composition of Crocin tablet?" |
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query2 = "Which medicine is used for fever and headache?" |
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query3 = "What are the side effects of Paracetamol?" |
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query4 = "Medicines available for diabetes in India" |
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# Process query |
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inputs = tokenizer.encode(query1, return_tensors="pt") |
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# Generate response |
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with torch.no_grad(): |
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outputs = model.generate( |
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inputs, |
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max_length=512, |
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num_return_sequences=1, |
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temperature=0.7, |
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do_sample=True, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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### Advanced Usage |
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```python |
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# For more controlled generation about Indian medicines |
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def generate_medicine_response(question, max_length=256): |
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prompt = f"Indian Medicine Query: {question}\nResponse:" |
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inputs = tokenizer.encode(prompt, return_tensors="pt") |
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outputs = model.generate( |
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inputs, |
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max_length=max_length, |
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num_return_sequences=1, |
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temperature=0.6, |
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do_sample=True, |
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top_p=0.9, |
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repetition_penalty=1.1 |
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) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return response.split("Response:")[-1].strip() |
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# Example usage |
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question = "What are the uses of Azithromycin tablets available in India?" |
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answer = generate_medicine_response(question) |
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print(answer) |
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``` |
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## 📊 Performance & Capabilities |
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### Supported Medical Areas |
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- **Indian Pharmaceuticals:** Comprehensive database of medicines available in India |
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- **Drug Compounds:** Active ingredients, chemical compositions, formulations |
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- **Symptom Analysis:** Symptom-to-medicine mapping and recommendations |
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- **Disease Information:** Common diseases and their standard treatments in India |
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- **Prescription Guidance:** Dosage, administration, and usage instructions |
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- **Drug Interactions:** Side effects and contraindications |
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- **Medicine Alternatives:** Generic and branded medicine alternatives |
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### Performance Metrics |
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- **Training Data:** Indian medicines dataset with comprehensive drug information |
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- **Specialization:** Focused on Indian pharmaceutical market and medicine availability |
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- **Coverage:** Extensive database of Indian medicines, their compounds, and uses |
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- **Accuracy:** High precision in Indian medicine information and drug compound details |
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## ⚠️ Important Medical Disclaimer |
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> **CRITICAL NOTICE:** This model is for informational and educational purposes only. It should NOT be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare providers for medical concerns. |
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### Limitations & Risks |
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- **Not a replacement for medical professionals** |
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- **May contain inaccuracies or outdated information** |
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- **Should not be used for emergency medical situations** |
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- **Requires human oversight for clinical applications** |
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- **May have biases present in training data** |
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## 🎯 Intended Use Cases |
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### ✅ Appropriate Uses |
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- Indian pharmaceutical research and education |
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- Medicine information lookup and comparison |
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- Drug compound analysis and research |
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- Symptom-to-medicine mapping assistance |
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- Prescription guidance and dosage information |
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- Medicine availability and alternatives research |
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- Healthcare app development and integration |
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### ❌ Inappropriate Uses |
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- Direct patient diagnosis |
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- Emergency medical decisions |
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- Prescription or treatment recommendations without medical supervision |
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- Replacement for clinical judgment |
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- Use without proper medical context |
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## 🔧 Technical Specifications |
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### Model Architecture |
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- **Base Architecture:** GPT-2 Transformer model |
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- **Fine-tuning:** Supervised learning on Indian medicines dataset |
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- **Context Length:** Standard GPT-2 context window |
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- **Training Approach:** Domain-specific fine-tuning on pharmaceutical data |
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### Training Details |
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- **Training Data:** Indian medicines dataset including: |
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- Medicine names and brand information |
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- Drug compounds and chemical compositions |
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- Symptom-medicine mappings |
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- Prescription guidelines and dosages |
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- Disease-treatment associations |
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- Side effects and contraindications |
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- **Training Regime:** Supervised fine-tuning on GPT-2 with pharmaceutical domain adaptation |
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- **Optimization:** Adam optimizer with learning rate scheduling |
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- **Data Focus:** Indian pharmaceutical market and medicine availability |
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## 📚 Datasets & Training |
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### Training Data Sources |
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- Comprehensive Indian medicines database |
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- Drug compound and chemical composition data |
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- Symptom-medicine relationship mappings |
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- Prescription guidelines and dosage information |
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- Disease-treatment associations |
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- Medicine availability and market data |
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### Data Preprocessing |
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- Medicine name normalization and standardization |
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- Drug compound data structure optimization |
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- Symptom-medicine relationship mapping |
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- Quality filtering and validation of pharmaceutical data |
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- Indian market-specific data curation |
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## 🧪 Evaluation & Validation |
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### Evaluation Metrics |
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- **Medicine Information Accuracy:** Correctness of drug compound and usage information |
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- **Symptom Mapping Precision:** Accuracy of symptom-to-medicine recommendations |
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- **Indian Market Relevance:** Appropriateness for Indian pharmaceutical context |
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- **Safety Assessment:** Risk evaluation for medicine information provision |
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### Benchmark Performance |
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- **Indian Medicine Database:** Comprehensive coverage of medicines available in India |
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- **Drug Compound Accuracy:** High precision in chemical composition information |
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- **Symptom-Medicine Mapping:** Effective symptom-to-treatment recommendations |
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## 🔄 Updates & Maintenance |
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This model is maintained and updated with: |
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- Latest Indian medicine information |
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- New drug approvals and market entries |
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- Updated compound and formulation data |
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- Enhanced symptom-medicine mappings |
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## 📖 Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{mayank2024indianmedicines, |
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title={Mayank-AI: Indian Medicines Information Model}, |
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author={Malviya, Mayank}, |
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year={2024}, |
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url={https://huggingface.co/Mayank-22/Mayank-AI}, |
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note={GPT-2 based model for Indian pharmaceutical information} |
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} |
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``` |
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## 🤝 Contributing |
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Contributions to improve the model are welcome! Please: |
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- Report issues with medicine information accuracy |
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- Suggest new Indian medicines to include |
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- Share feedback on drug compound data |
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- Contribute to symptom-medicine mapping improvements |
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## 📞 Contact & Support |
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- **Model Author:** Mayank Malviya |
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- **Repository:** [Mayank-22/Mayank-AI](https://huggingface.co/Mayank-22/Mayank-AI) |
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- **Issues:** Please report issues through the Hugging Face repository |
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## 📄 License |
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This model's license is not currently specified. Please check the repository or contact the author for licensing information. |
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## 🙏 Acknowledgments |
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Special thanks to the Indian pharmaceutical community, healthcare professionals, and medical researchers who contributed to the development and validation of this specialized model for Indian medicines. |
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--- |
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**Remember:** This AI model is a tool to assist, not replace, medical professionals. Always prioritize patient safety and seek professional medical advice for healthcare decisions. |
