metadata

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

pip install transformers torch

Example Inference

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

[email protected]

Citing this Model

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

@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

Acknowledgments

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