ProdocAI/HealthIntent-Classifier-1b-GGUF-v1
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.
Ollama Integration
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.
Model Details
- Model Name:
Intent-classification-1b-GGUF-v1
- Base Model:
Llama3.2-1B
- Number of Parameters: 1 Billion
- Dataset: Custom dataset of healthcare-related conversation
- Training Data Statistics:
- Languages: Includes
en
,mr
,te
,hi
,bn
, among others (detailed in Language Map section)
- Languages: Includes
Model Objective
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.
Supported Intents
- Appointment Booking
- Surgery Enquiry
- Emergency Assistance
- Lab Test Results Inquiry
- Symptom Consultation
- Hospital Services Enquiry
- Job or Internship Enquiry
- Complaint or Feedback
- Health Check-Up Packages
- Health Insurance Enquiry
- Irrelevant Query
Dataset Overview
The dataset includes 3,000 unique conversations mapped to the above intents. Conversations are provided in multiple languages to support diverse user bases.
Example Input Format
Input to the model should be provided in the following format:
"Below is the conversation between the bot and user
user: Hello
bot: Hello! I am Aisha, a Health-Care Executive at Madhavbaug. How can I assist you today with your health concern?
user: Is my appointment confirmed for tomorrow
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?
user: ๐
bot: I can help you check the status of your appointment. Please provide me with your name and contact number.
user: Krishnan Iyer
9323195487
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
user: Booking is already done for tomorrow 10 am
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.
user: Thane
bot: Oh great! We have found 20 expert doctors in your area. Could you please let us know your exact location in Thane?
user: Majiwada Thane West
bot: Clinic list Sent
user: โ
Your appointment has been successfully booked!
*Appointment Details:*
*Clinic Name:*
Kolshet Road (Thane W )
*Date & Time:*
19-Oct-2024 10:00
*Clinic Address:*
Shop no.102, First floor, Krishna towers, above Sanman hotel kolshet road, kapurbawdi,Thane West, Thane, MAHARASHTRA - 400607
Thank you for booking appointment with us!
We shall call you shortly to confirm your appointment.
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!
user: Hope it is confirmed
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!
user: Thanks"
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Model tree for ProdocAI/Intent-classification-1b-GGUF-v1
Base model
meta-llama/Llama-3.2-1B-Instruct