---
language: en
license: other
tags:
- t5
- nlp
- plot-suggestion
- conditional-generation
- machine-learning
inference: true
datasets:
- custom
model-index:
- name: T5 48 Sectors Plot Suggestion Model
  results: []
---

# T5 48 Sectors Plot Suggestion Model

## 🚨 Usage Restrictions Notice

**IMPORTANT: This model is NOT freely available for unrestricted use.**
- Prior written permission is REQUIRED before using this model
- Commercial use is strictly prohibited without explicit authorization
- Academic or research use requires formal permission from the model's creator

## Model Description

### Model Details
- **Developed by:** Mageswaran
- **Model type:** T5 Fine-Tuned Conditional Generation Model
- **Base Model:** T5
- **Specialized Task:** Plot Suggestion Generation
- **Language(s):** English

### Model Purpose
The T5 48 Sectors Plot Suggestion Model is designed to generate plot suggestions based on sector-specific inputs. By leveraging the T5 model's powerful conditional generation capabilities, it can provide contextually relevant plot ideas tailored to specific sectors and column characteristics.

## Intended Use

### Primary Use Cases
- Automated plot suggestion generation
- Creative writing assistance
- Sector-specific narrative ideation
- Data-driven storytelling

### Out of Scope
- Real-time production inference without permission
- Commercial applications without explicit licensing
- Use in sensitive or critical decision-making processes without validation

## Technical Specifications

### Model Architecture
- **Base Model:** T5 (Text-to-Text Transfer Transformer)
- **Fine-Tuning:** Custom dataset across 48 sectors
- **Input Format:** Sector label and column names
- **Output:** Contextually relevant plot suggestions

### Generation Capabilities
- **Maximum Output Length:** 500 tokens
- **Input Processing:** Sector-aware generation
- **Contextual Understanding:** Leverages sector-specific nuances

## Usage

### Installation
```bash
pip install transformers torch
```

### Example Inference
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

# Load the T5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained("Mageswaran/t5_48_sectors")
model = T5ForConditionalGeneration.from_pretrained("Mageswaran/t5_48_sectors")

# Move model to device
model.to(device)

def perform_inference(input_text, max_length=500):
    # Tokenize the input text
    input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)
    
    # Generate output from the model
    output_ids = model.generate(input_ids, max_length=max_length)
    
    # Decode the generated output into text
    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return output_text

# Example usage
input_data = {
    "input": {
        "label": "Film & Television",
        "columns": "movie_duration, genre, audience_rating"
    }
}

input_text = f"label: {input_data['input']['label']}, columns: {input_data['input']['columns']}"
plot_suggestion = perform_inference(input_text)
print(plot_suggestion)
```

## Limitations and Potential Biases

### Known Limitations
- Generated plots are based on training data
- Creativity is constrained by model's learned patterns
- Potential for repetitive or generic suggestions
- Performance varies across different sectors

### Potential Biases
- Inherent biases from training dataset
- May reflect cultural or demographic representations in source data
- Limited by the diversity of training examples

## Ethical Considerations

- Transparency about model capabilities
- Emphasis on creative assistance, not replacement
- Strict usage controls
- Commitment to responsible AI deployment

## Licensing and Permissions

### Usage Restrictions
- Prior written permission REQUIRED
- Commercial use strictly prohibited
- Academic use requires formal authorization

### Permissions Inquiry
To request model usage, contact:
- **Email:** [Your Contact Email]
- **Hugging Face Profile:** [Your Hugging Face Profile URL]

## Contact

meyyappanmageswaran@gmail.com

## Citing this Model

If you use this model in your research, please cite using the following BibTeX entry:

```bibtex
@misc{mageswaran_t5_48_sectors_plot,
  title = {T5 48 Sectors Plot Suggestion Model},
  author = {Mageswaran},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Mageswaran/t5_48_sectors}}
}
```

## Additional Resources

- [Author's Hugging Face Profile](https://huggingface.co/Mageswaran)
- [Model Repository](https://huggingface.co/Mageswaran/t5_48_sectors)

## Acknowledgments

- Hugging Face Transformers
- T5 Model Developers
- Open-source Machine Learning Community