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---
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license: mit
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language: multilingual
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base_model: unsloth/Llama-3.2-1B-Instruct
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quantized_by: prodoc.ai
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tags:
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- classification
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- intent
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- healthcare
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- conversational
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- LLM
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widget:
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- text: "I want to book an appointment for my child."
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- text: "Can you help me with lab test results?"
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---
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# ProdocAI/HealthIntent-Classifier-1b-GGUF-v1
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`HealthIntent-Classifier-1b-GGUF-v1` is a fine-tuned version of the `Llama3.2-1B` model, trained to classify healthcare-related queries into predefined intents. This model is designed to streamline user interactions by identifying their specific healthcare-related needs efficiently.
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# Ollama Integration
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Leverage seamless integration with Ollama, where the model is fully hosted and ready to run. Simply execute the command `ollama run Prodoc/intent-classification-1b` to start utilizing the model's intent classification capabilities. Transform your healthcare conversational systems with precision and ease.
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## Model Details
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- **Model Name**: `Intent-classification-1b-GGUF-v1`
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- **Base Model**: `Llama3.2-1B`
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- **Number of Parameters**: 1 Billion
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- **Dataset**: Custom dataset of healthcare-related conversation
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- **Training Data Statistics**:
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- **Languages**: Includes `en`, `mr`, `te`, `hi`, `bn`, among others (detailed in Language Map section)
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## Model Objective
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The model identifies the intent behind healthcare-related queries and classifies them into one of the predefined categories. This allows for better routing and handling of user requests in healthcare systems.
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### Supported Intents
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1. **Appointment Booking**
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2. **Surgery Enquiry**
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3. **Emergency Assistance**
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4. **Lab Test Results Inquiry**
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5. **Symptom Consultation**
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6. **Hospital Services Enquiry**
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7. **Job or Internship Enquiry**
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8. **Complaint or Feedback**
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9. **Health Check-Up Packages**
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10. **Health Insurance Enquiry**
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11. **Irrelevant Query**
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## Dataset Overview
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The dataset includes 3,000 unique conversations mapped to the above intents. Conversations are provided in multiple languages to support diverse user bases.
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### Example Input Format
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Input to the model should be provided in the following format:
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```plaintext
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"Below is the conversation between the bot and user
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user: Hello
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bot: Hello! I am Aisha, a Health-Care Executive at Madhavbaug. How can I assist you today with your health concern?
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user: Is my appointment confirmed for tomorrow
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bot: I can help you with that. Can you please provide me with your name and contact number so I can check the appointment status for you?
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user: 👆
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bot: I can help you check the status of your appointment. Please provide me with your name and contact number.
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user: Krishnan Iyer
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9323195487
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bot: Current location, city name, or postal code is required to book an appointment, please provide your location details so I can assist you further
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user: Booking is already done for tomorrow 10 am
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bot: It seems that your message does not include information about your location. Could you please provide your current city name or postal code? This will help me assist you better in booking an appointment.
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user: Thane
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bot: Oh great! We have found 20 expert doctors in your area. Could you please let us know your exact location in Thane?
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user: Majiwada Thane West
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bot: Clinic list Sent
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user: ✅ Your appointment has been successfully booked!
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*Appointment Details:*
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*Clinic Name:*
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Kolshet Road (Thane W )
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*Date & Time:*
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19-Oct-2024 10:00
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*Clinic Address:*
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Shop no.102, First floor, Krishna towers, above Sanman hotel kolshet road, kapurbawdi,Thane West, Thane, MAHARASHTRA - 400607
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Thank you for booking appointment with us!
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We shall call you shortly to confirm your appointment.
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bot: Your appointment has been successfully booked at Kolshet Road (Thane W) clinic on 19-Oct-2024 at 10:00. The clinic address is Shop no.102, First floor, Krishna towers, above Sanman hotel kolshet road, kapurbawdi, Thane West, Thane, MAHARASHTRA - 400607. Thank you for booking the appointment!
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user: Hope it is confirmed
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bot: Yes, your appointment at Kolshet Road (Thane W) clinic on 19-Oct-2024 at 10:00 is confirmed. Thank you for booking the appointment!
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user: Thanks"
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