Update README.md

README.md

@@ -1,199 +1,183 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- healh
+- STD
+- STI
+language:
+- en
 ---
 
 # Model Card for Model ID
+STD-MedGuide is an AI model designed to answer questions about sexually transmitted diseases (STDs/MSTs) in clear, natural language.
 
-<!-- Provide a quick summary of what the model is/does. -->
+You give it a question (e.g., “What are the common symptoms of chlamydia?”).
 
+The model processes the question and generates a medically relevant answer, based on knowledge it learned from medical QA datasets.
+
+It is intended for education and awareness, not as a substitute for professional medical advice.
 
 
 ## Model Details
+🛠️ Model Details – STD-MedGuide
 
-### Model Description
+Model Name: STD-MedGuide
 
-<!-- Provide a longer summary of what this model is. -->
+Base Architecture: FLAN-T5 (small) — a text-to-text transformer model designed for instruction-following tasks.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Task: Question Answering (QA) focused on sexually transmitted diseases (STDs/MSTs).
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Dataset Used:
 
-### Model Sources [optional]
+CareQA (open-ended medical QA dataset)
 
-<!-- Provide the basic links for the model. -->
+Medical-QA (open-ended medical QA dataset)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Both datasets adapted for French MST terminology and general STD questions.
 
-## Uses
+Training Environment: Google Colab using Hugging Face transformers library.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Training Setup:
 
-### Direct Use
+Epochs: 2 planned (beta version checkpoint at step ~171)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+Batch Size: 4–8 (depending on GPU/CPU availability)
 
-### Downstream Use [optional]
+Learning Rate: 5e-5
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Checkpointing: every 100 steps (to save progress and allow beta sharing)
 
-[More Information Needed]
+Device: GPU if available (Colab runtime), otherwise CPU
 
-### Out-of-Scope Use
+Loss Performance:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Initial training loss ~21
 
-[More Information Needed]
+Reduced to ~0.5 in early steps (checkpoint 171)
 
-## Bias, Risks, and Limitations
+Tokenizer: AutoTokenizer from Hugging Face, compatible with FLAN-T5 small.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Output Type: Text generation — given a question, generates an answer in natural language.
 
-[More Information Needed]
+Intended Users: Students, educators, awareness programs, and general audiences seeking information about STDs.
 
-### Recommendations
+Limitations: Not suitable for clinical diagnosis; probabilistic output; may reflect dataset biases.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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
+### Model Description
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+STD-MedGuide is a specialized question-answering model designed to provide reliable and accessible information about sexually transmitted diseases (STDs/MSTs). Built on top of the FLAN-T5 architecture, it has been fine-tuned using medical QA datasets to understand and respond to natural language questions related to sexual health.
 
-[More Information Needed]
+The model’s primary function is to take a user’s question — for example, “Quels sont les symptômes les plus courants des MST ?” or “How can HIV be prevented?” — and generate a concise, contextually relevant answer. By focusing specifically on STD-related queries, STD-MedGuide can assist students, educators, and awareness programs in spreading accurate information about prevention, symptoms, transmission methods, and general health education.
 
-### Training Procedure
+While it produces medically informed responses, STD-MedGuide is not a diagnostic or clinical tool. Instead, it is intended as an educational resource, making medical knowledge more approachable and helping raise awareness about sensitive health topics. Its potential applications include supporting classroom learning, contributing to health campaigns, and serving as an interactive guide for individuals seeking general knowledge about STDs.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
+- **Developed by:** [Saint-Vil Angie-Reyna Leddycia]
 
-[More Information Needed]
+## Uses
 
+🏷️ Uses / Applications of STD-MedGuide
 
-#### Training Hyperparameters
+Educational Tool
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Helps students, teachers, and healthcare trainees learn about sexually transmitted diseases.
 
-#### Speeds, Sizes, Times [optional]
+Can be integrated into classroom exercises, quizzes, or self-study activities.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Awareness and Prevention Campaigns
 
-[More Information Needed]
+Provides clear, understandable answers for public health awareness.
 
-## Evaluation
+Useful for NGOs, health organizations, or school programs promoting sexual health.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Interactive Question-Answering
 
-### Testing Data, Factors & Metrics
+Can serve as a chatbot or interactive assistant to answer general STD questions.
 
-#### Testing Data
+Supports engagement in workshops, seminars, or online learning platforms.
 
-<!-- This should link to a Dataset Card if possible. -->
+Reference for Research or Reports
 
-[More Information Needed]
+Can generate concise explanations of STD symptoms, transmission, and prevention for reports, presentations, or educational materials.
 
-#### Factors
+Language Adaptation
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Handles French terminology (MST) and English (STD), making it suitable for bilingual or international educational settings.
 
-[More Information Needed]
+Note: While STD-MedGuide is a powerful educational tool, it is not a substitute for professional medical advice or clinical diagnosis.
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+## Bias, Risks, and Limitations
 
-[More Information Needed]
+⚠️ Bias, Risks, and Limitations
 
-### Results
+Bias in Training Data
 
-[More Information Needed]
+STD-MedGuide is fine-tuned on datasets like CareQA and Medical-QA, which may reflect biases in medical literature, language, or cultural context.
 
-#### Summary
+Certain diseases, symptoms, or populations may be over- or under-represented, affecting the accuracy or completeness of answers.
 
+Medical Accuracy and Reliability
 
+The model generates responses based on patterns in the training data, not on real-time clinical knowledge.
 
-## Model Examination [optional]
+Answers may occasionally be incomplete, outdated, or partially incorrect, so it cannot replace professional medical advice.
 
-<!-- Relevant interpretability work for the model goes here -->
+Language and Context Limitations
 
-[More Information Needed]
+While the model handles French (MST terminology) and English reasonably well, complex questions or nuanced phrasing may lead to misinterpretation.
 
-## Environmental Impact
+It may not fully understand ambiguous questions or rare medical scenarios.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Ethical and Safety Risks
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Users could misinterpret answers as formal medical guidance.
 
-- **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]
+There is a risk of over-reliance, especially by individuals seeking medical help without consulting healthcare professionals.
 
-## Technical Specifications [optional]
+Technical Limitations
 
-### Model Architecture and Objective
+The current beta version is trained on a limited number of steps (~171), meaning it has not seen the full dataset and could improve with further training.
 
-[More Information Needed]
+Responses are generated probabilistically, so the model may produce different answers to the same question on repeated queries.
 
-### Compute Infrastructure
+Conclusion: STD-MedGuide is best used as an educational and awareness tool, to support learning and information dissemination about STDs, but not for diagnosis or clinical decision-making.
 
 [More Information Needed]
 
-#### Hardware
+### Recommendations
+💡 Recommendations
 
-[More Information Needed]
+Further Training
 
-#### Software
+Continue training on the full dataset to improve accuracy and coverage of STD-related questions.
 
-[More Information Needed]
+Consider incorporating additional French-language medical datasets to improve bilingual performance.
 
-## Citation [optional]
+Regular Updates
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Periodically update the model with new medical knowledge and guidelines to ensure answers stay current.
 
-**BibTeX:**
+Evaluation and Validation
 
-[More Information Needed]
+Evaluate on a larger, diverse validation set to measure performance and reduce bias.
 
-**APA:**
+Consider human-in-the-loop review to check medical accuracy of generated answers.
 
-[More Information Needed]
+Responsible Use
 
-## Glossary [optional]
+Use the model primarily for education and awareness, not for clinical decision-making.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Always include disclaimers when deploying the model to inform users of its limitations.
 
-[More Information Needed]
-
-## More Information [optional]
+User Interface & Accessibility
 
-[More Information Needed]
+Integrate into chatbots, educational platforms, or mobile apps for easier access.
 
-## Model Card Authors [optional]
+Consider adding multilingual support beyond French and English.
 
-[More Information Needed]
+Bias Mitigation
 
-## Model Card Contact
+Monitor for biases in answers (e.g., population, gender, age) and refine the training dataset accordingly.
 
-[More Information Needed]
+Encourage feedback from users to improve answer fairness and relevance.