---
base_model:
- valhalla/t5-base-qg-hl
pipeline_tag: text2text-generation
---

# I-Comprehend Question Generation Model

## Overview

The **I-Comprehend Question Generation Model** is a T5-based model designed to generate questions from a given context and answer. This model is particularly useful for educational purposes, automated content creation, and enhancing reading comprehension tools.

## Model Details

- **Model Architecture:** T5 (Text-to-Text Transfer Transformer)
- **Model Type:** Conditional Generation
- **Training Data:** [Specify the dataset or type of data used for training]
- **Use Cases:** Question generation, educational tools, content creation

## Installation

To use this model, you need to have the `transformers` library installed. You can install it via pip:

```bash
pip install transformers
```

## Usage

To use the model, load it with the appropriate tokenizer and model classes from the `transformers` library. Ensure you have the correct repository ID or local path.

```bash
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load the model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("miiiciiii/I-Comprehend_qg")
tokenizer = T5Tokenizer.from_pretrained("miiiciiii/I-Comprehend_qg")

def get_question(context, answer, model, tokenizer):
    """Generate a question for the given answer and context."""
    answer_span = context.replace(answer, f"<hl>{answer}<hl>", 1) + "</s>"
    inputs = tokenizer(answer_span, return_tensors="pt")
    question = model.generate(input_ids=inputs.input_ids, max_length=50)[0]

    return tokenizer.decode(question, skip_special_tokens=True)

# Define the context and answer
context = "The Eiffel Tower is located in Paris and is one of the most famous landmarks in the world."
answer = "Eiffel Tower"

# Generate the question
question = get_question(context, answer, model, tokenizer)
print("Generated Question:", question)
```

## Model Performance

- **Evaluation Metrics:** [BLEU, ROUGE]
- **Performance Results:** [Accuracy]

## Limitations

- The model may not perform well on contexts that are significantly different from the training data.
- It may generate questions that are too generic or not contextually relevant in some cases.

## Contributing

We welcome contributions to improve the model or expand its capabilities. Please feel free to open issues or submit pull requests.

## License

[MIT License]

## Acknowledgments

- [Acknowledge any datasets, libraries, or collaborators that contributed to the model]

## Contact

For any questions or issues, please contact [icomprehend.system@gmail.com].