---
base_model:
- google-t5/t5-base
pipeline_tag: question-answering
license: mit
datasets:
- rajpurkar/squad_v2
metrics:
- accuracy
library_name: transformers
---

# I-Comprehend Answer Generation Model

## Overview

The **I-Comprehend Answer Generation Model** is a T5-based model designed to generate answers from a given question and context. This model is particularly useful for applications in automated question answering systems, educational tools, and enhancing information retrieval processes.

## 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:** Answer generation, question answering systems, educational tools

## Installation

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

```bash
pip install transformers
pip install torch
```

## 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
import torch

# Load the model and tokenizer
t5ag_model = T5ForConditionalGeneration.from_pretrained("miiiciiii/I-Comprehend_ag")
t5ag_tokenizer = T5Tokenizer.from_pretrained("miiiciiii/I-Comprehend_ag")

def answer_question(question, context):
    """Generate an answer for a given question and context."""
    input_text = f"question: {question} context: {context}"
    input_ids = t5ag_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
    
    with torch.no_grad():
        output = t5ag_model.generate(input_ids, max_length=512, num_return_sequences=1, max_new_tokens=200)
    
    return t5ag_tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
question = "What is the location of the Eiffel Tower?"
context = "The Eiffel Tower is located in Paris and is one of the most famous landmarks in the world."
answer = answer_question(question, context)
print("Generated Answer:", answer)
```

## 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 answers 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].