ms-hardwick-translator

This model is a fine-tuned version of google/flan-t5-base trained to rewrite English text into the style and tone of Melissa Fulmore-Hardwick.

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Model description

The ms-hardwick-translator adapts written English to match Melissa Fulmore-Hardwick’s voice — her choice of words, sentence rhythm, and instructional style.
It is built on the Flan-T5 Base architecture, a sequence-to-sequence transformer model optimized for instruction following and natural language generation.

• Architecture: T5 (encoder-decoder)
• Base model: google/flan-t5-base
• Language: English
• Task: Style translation / text rewriting
• Framework: Hugging Face Transformers

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Intended uses & limitations

Intended uses

• Convert standard text into Melissa Fulmore-Hardwick’s instructional style for lesson materials.
• Create consistent tone for educational resources or projects that require her voice.

Limitations

• May not preserve highly technical terms if they were not present in the training set.
• Not intended for literal translation between different human languages.
• Works best for short to medium-length text (1–3 paragraphs).

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Training and evaluation data

• Dataset type: Custom parallel dataset with two columns:
  • input: Original English sentence.
  • target: Sentence rewritten in Melissa Fulmore-Hardwick’s voice.
• Dataset size: [Insert row count]
• Source: Curated examples from transcripts, speeches, and rewritten passages.

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Training procedure

Preprocessing

• Tokenized with AutoTokenizer from google/flan-t5-base
• Maximum input length: [Insert max length used]
• Maximum target length: [Insert max length used]

Training hyperparameters

• learning_rate: 0.0003
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• optimizer: AdamW
• epochs: [Insert number if known]

Frameworks

• Transformers
• Datasets
• Trainer API

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Example usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ClergeF/ms-hardwick-translator")
model = AutoModelForSeq2SeqLM.from_pretrained("ClergeF/ms-hardwick-translator")

text = "Please complete your homework by tomorrow."
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))