t5-small-sentence-validator

🧾 Model Card — T5-Small Sentence Validator

This is the model card of a 🤗 Transformers model fine-tuned for text normalization — restoring punctuation, capitalization, and proper spacing from raw transcripts or unformatted text.

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👨‍💻 Developed by

Pradhap Rajamani

Funded by : Independent academic project — Purdue University (Master’s in Computer Science)
Shared by : Pradhap Rajamani

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🧠 Model Details

• Model type: Sequence-to-sequence text generation model (T5-small architecture)
• Language(s): English 🇺🇸
• License: Apache 2.0
• Finetuned from model: t5-small

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🔗 Model Sources

• Repository: Hugging Face Hub – pradhap1125/t5-small-sentence-validator
• Base Paper: “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”

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🎯 Uses

Direct Use

Automatically adds missing punctuation, capitalization, and spacing to unformatted English text.

Example:

Input: normalize: helloeveryonewelcome to todayssession

Output: Hello everyone, welcome to today's session.

Downstream Use

Text cleanup for ASR and transcripts

Preprocessing for NLP tasks (NER, summarization, etc.)

Chatbot or conversation logs normalization

🚫 Out-of-Scope Use

Grammar correction beyond punctuation

Rewriting semantics or paraphrasing

⚠️ Limitations and Biases

Limitation Description

Recommendation:

Always perform a quick review before deploying results in production systems.

🚀 How to Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "pradhap1125/t5-small-sentence-validator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

text = "helloeveryonewelcome to todayssession"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧩 Training Details

🗂 Dataset

A custom English dataset derived from Wikipedia sentences. Text was noisified by removing punctuation, capitalization, and spacing to simulate ASR output.

🧮 Hyperparameters

Training Environment

Evaluation

Metrics

Qualitative Example

🧑‍💻 Model Card Authors

Pradhap Rajamani

Contact

📧 https://www.linkedin.com/in/pradhap-rajamani/ 📧 https://github.com/pradhap1125

Repo

https://github.com/pradhap1125/t5-small-sentence-validator