Menstrual Health Awareness Chatbot

🚀 Model Card for Menstrual Health Awareness Model

This model is designed to answer menstrual - health - related questions using a Q&A dataset on the topic, offering valuable information in the field of menstrual health.

🚀 Quick Start

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("adi2606/Menstrual-Health-Awareness-Chatbot")
model = AutoModelForSeq2SeqLM.from_pretrained("adi2606/Menstrual-Health-Awareness-Chatbot")

# Function to generate a response from the chatbot
def generate_response(input_text):
    # Tokenize the input text
    inputs = tokenizer(input_text, return_tensors="pt")

    # Generate the output (the response)
    outputs = model.generate(**inputs)

    # Decode the output back to text
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Example usage
input_text = "What are the symptoms of PMS?"
response = generate_response(input_text)
print(response)

✨ Features

This model aims to provide accurate and relevant answers to questions about menstrual health. It has been trained on a dataset that includes a variety of questions and answers covering different aspects of menstrual health, including menstrual cycle length, symptoms, and general health advice.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("adi2606/Menstrual-Health-Awareness-Chatbot")
model = AutoModelForSeq2SeqLM.from_pretrained("adi2606/Menstrual-Health-Awareness-Chatbot")

# Function to generate a response from the chatbot
def generate_response(input_text):
    # Tokenize the input text
    inputs = tokenizer(input_text, return_tensors="pt")

    # Generate the output (the response)
    outputs = model.generate(**inputs)

    # Decode the output back to text
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Example usage
input_text = "What are the symptoms of PMS?"
response = generate_response(input_text)
print(response)

📚 Documentation

Uses

Direct Use

The model can be used to answer questions related to menstrual health. Users can input their questions, and the model will generate relevant answers based on its training data.

Out-of-Scope Use

⚠️ Important Note

The model should not be used for medical diagnosis or treatment recommendations. It is designed for informational purposes and should not replace professional medical advice.

Bias, Risks, and Limitations

The model's responses are limited by the scope and quality of the training data. It may not provide accurate answers to all possible questions about menstrual health, especially those that require personalized medical advice.

💡 Usage Tip

Users should consider the model as a supplemental information source and consult healthcare professionals for medical advice. Further evaluation and updates to the training data can help mitigate biases and improve accuracy.

Training Details

Training Data

The model was trained on the Menstrual Health Awareness Dataset, which includes questions and answers related to menstrual health.

Training Procedure

Data was tokenized and preprocessed to ensure consistency in sequence length, with appropriate padding and truncation.

Training Hyperparameters

• Training regime: fp32 mixed precision
• Learning rate: 2e - 5
• Batch size: 16
• Epochs: 3
• Weight decay: 0.01

Evaluation

Testing Data

The model was evaluated on a test split of the Menstrual Health Awareness Dataset.

Factors

The evaluation considered the accuracy and relevance of the answers generated by the model.

Metrics

• Accuracy: The percentage of correct answers generated by the model.
• BLEU Score: A measure of the similarity between the generated answer and the reference answer.

Results

The model achieved an accuracy of 85% and a BLEU score of 70 on the test dataset, indicating a high level of relevance and correctness in its responses.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: NVIDIA Tesla V100
• Cloud Provider: Google Cloud
• Compute Region: us - central1
• Carbon Emitted: 15 kg CO2eq (estimated)

Compute Infrastructure

Hardware

GPU: NVIDIA Tesla V100

Software

• Framework: Transformers library by Hugging Face
• Python Version: 3.8

📄 License

This project is licensed under the MIT License.