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Model Details
Model Description
This model is a fine-tuned version of google/flan-t5-small designed for medication-related question answering. The model was fine-tuned using a medication details dataset formatted as instruction-response examples. The goal of the model is to generate short educational responses to medication questions, such as what a medication is used for or what side effects may be associated with it.
This model was created for an academic final project and is intended for educational demonstration only. It should not be used as medical advice or as a replacement for a pharmacist, physician, or official drug information source.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Uses
This model can be used to generate short educational answers to basic medication-related questions. Example questions include:
- What is Metformin used for?
- What are the side effects of Ibuprofen?
- What is Atorvastatin used for?
The model is intended for demonstration and learning purposes.
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
This model should not be used for diagnosis, treatment decisions, medication selection, dosing recommendations, emergency medical questions, or professional clinical decision-making. It should not replace official drug references or advice from licensed healthcare professionals.
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Bias, Risks, and Limitations
This model has several limitations. It was fine-tuned on a limited medication dataset and may generate incomplete, outdated, or incorrect information.
The model does not verify answers against current clinical guidelines, drug databases, patient-specific information, allergies, medical history, or drug interactions.
Users should verify all medication information with a pharmacist, physician, or official drug reference.
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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
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "melisar2/pharmassist-flan-t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
question = "What is Metformin used for?"
inputs = tokenizer(question, return_tensors="pt", truncation=True)
outputs = model.generate(
**inputs,
max_new_tokens=80,
num_beams=4
)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)
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Training Details
Training Data
The model was fine-tuned using the Medicine_Details.csv dataset.
The dataset contains medication-related information and was formatted into instruction-response examples for training.
The project used fields such as medication name, uses, and side effects to create medication question-answering prompts.
https://www.kaggle.com/datasets/swathigunti/medicine-details-csv
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
ROUGE scores were used to compare the model-generated answers with the expected reference answers. ROUGE is appropriate for this project because the task is text generation, and the evaluation compares generated text to target medication-related responses.
ROUGE Scores:
ROUGE1:
F-measure: 0.4037
Precision: 0.4806
Recall: 0.3996
ROUGE2:
F-measure: 0.2646
Precision: 0.3011
Recall: 0.2684
ROUGEL:
F-measure: 0.3837
Precision: 0.4520
Recall: 0.3815
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Software
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