formal-lang-rxcx-model

Formal Language T5 Model

This model is fine-tuned from T5-base for formal language correction and text formalization.

Model Description

• Model Type: T5-base fine-tuned
• Language: English
• Task: Text Formalization and Grammar Correction
• License: Apache 2.0
• Base Model: t5-base

Intended Uses & Limitations

Intended Uses

• Converting informal text to formal language
• Improving text professionalism
• Grammar correction
• Business communication enhancement
• Academic writing improvement

Limitations

• Works best with English text
• Maximum input length: 128 tokens
• May not preserve specific domain terminology
• Best suited for business and academic contexts

Usage

from transformers import AutoModelForSeq2SeqGeneration, AutoTokenizer

model = AutoModelForSeq2SeqGeneration.from_pretrained("renix-codex/formal-lang-rxcx-model")
tokenizer = AutoTokenizer.from_pretrained("renix-codex/formal-lang-rxcx-model")

# Example usage
text = "make formal: hey whats up"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
formal_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

Example Inputs and Outputs

Training

The model was trained on the Grammarly/COEDIT dataset with the following specifications:

• Base Model: T5-base
• Training Hardware: A100 GPU
• Sequence Length: 128 tokens
• Input Format: "make formal: [informal text]"

License

Apache License 2.0

Citation

@misc{formal-lang-rxcx-model,
    author = {renix-codex},
    title = {Formal Language T5 Model},
    year = {2024},
    publisher = {HuggingFace},
    journal = {HuggingFace Model Hub},
    url = {https://huggingface.co/renix-codex/formal-lang-rxcx-model}
}

Developer

Model developed by renix-codex

Ethical Considerations

This model is intended to assist in formal writing while maintaining the original meaning of the text. Users should be aware that:

• The model may alter the tone of personal or culturally specific expressions
• It should be used as a writing aid rather than a replacement for human judgment
• The output should be reviewed for accuracy and appropriateness

Updates and Versions

Initial Release - February 2024

• Base implementation with T5-base
• Trained on Grammarly/COEDIT dataset
• Optimized for formal language conversion