Model Card for Model ID
Model Details
Model Description
This model is designed to classify customer queries in the healthcare domain. The model classifies the following aspects of a healthcare-related query:
- Intent: The purpose or action requested in the query (e.g.,
Appointment Rescheduling, Insurance Claim Assistance).
- Department: The department to which the query is related (e.g.,
Pediatrics, ENT, Dermatology).
- Level: The urgency level of the query (
Medium, Soon, Emergency).
This model is based on a T5 architecture fine-tuned on a dataset of 3,800 entries of healthcare customer queries, and it helps in building automated healthcare assistants, query triaging systems, or customer support tools that understand and categorize user requests.
- Developed by: Ataur Rahman Likhon
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: Text Classification
- Language(s) (NLP): English
- License: [More Information Needed]
- Finetuned from model [optional]: T5
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
The model can be used to classify customer queries in healthcare-related domains, providing businesses with a tool to automatically triage and categorize queries to the right department or team. It can be integrated into chatbot applications or support systems for better customer interaction.
Downstream Use [optional]
The model can be fine-tuned further for specific use cases such as insurance claim processing or appointment booking systems within the healthcare industry.
Out-of-Scope Use
This model is not suitable for use in non-healthcare contexts or for queries outside the scope of customer service in healthcare.
Bias, Risks, and Limitations
The model may exhibit biases in how it handles queries from underrepresented departments or urgent levels. It may also not perform well on queries that deviate significantly from the provided dataset or that require highly specialized knowledge outside of the healthcare domain.
Recommendations
Users should be aware that the model might struggle with out-of-scope queries or rare edge cases. It is recommended to continuously monitor and update the model based on user feedback to improve performance over time.
How to Get Started with the Model
Use the following code to load and use the model:
1from transformers import T5Tokenizer, T5ForConditionalGeneration
2
3# Load pre-trained model
4model = T5ForConditionalGeneration.from_pretrained('path_to_model')
5tokenizer = T5Tokenizer.from_pretrained('path_to_tokenizer')
6
7# Example usage
8input_text = "How do I proceed with insurance claim assistance in ENT?"
9inputs = tokenizer(input_text, return_tensors="pt")
10outputs = model.generate(inputs['input_ids'])
11decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
12print(decoded_output)
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
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
Training Data
[More Information Needed]
Training Procedure
-Training Hyperparameters
-
- Training regime: fp16 mixed precision
-
Speeds, Sizes
-
- The training was conducted on a medium-scale machine with multiple GPUs.
Preprocessing [optional]
The training data was preprocessed by tokenizing the text using a T5 tokenizer, and the data was split into training and validation sets for fine-tuning.
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
The evaluation considered different domains (departments) and urgency levels as factors to assess the model's robustness.
[More Information Needed]
Metrics
The model was evaluated using accuracy, precision, recall, and F1-score for each category: intent, department, and urgency.
Results
The model achieved an accuracy of 92% in classifying intents, 90% for department classification, and 88% for urgency classification.
Summary
The model performs well across the three categories with good generalization to unseen queries in the healthcare domain.
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
