Model Card for Menstrual Health Awareness Model
This model is designed to answer questions related to menstrual health using a dataset of questions and answers on the topic.
Model Use Guide
1from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
2
3# Load the tokenizer and model
4tokenizer = AutoTokenizer.from_pretrained("adi2606/Menstrual-Health-Awareness-Chatbot")
5model = AutoModelForSeq2SeqLM.from_pretrained("adi2606/Menstrual-Health-Awareness-Chatbot")
6
7# Function to generate a response from the chatbot
8def generate_response(input_text):
9 # Tokenize the input text
10 inputs = tokenizer(input_text, return_tensors="pt")
11
12 # Generate the output (the response)
13 outputs = model.generate(**inputs)
14
15 # Decode the output back to text
16 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
17 return response
18
19# Example usage
20input_text = "What are the symptoms of PMS?"
21response = generate_response(input_text)
22print(response)
Model Description
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.
- Model type: Sequence-to-sequence language model
- Language(s) (NLP): English
- License: MIT
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
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.
Recommendations
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, Factors & Metrics
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
Summary
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
