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Re-Punctuate
SJ-Ray/Re-Punctuate
Re-Punctuate is a machine learning model from SJ-Ray. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Mar 16, 2022
- Updated
- Jun 29, 2022
- SHA
- a43b72cf
Model card
From the Hugging Face model README
<h2>Re-Punctuate:</h2>
Re-Punctuate is a T5 model that attempts to correct Capitalization and Punctuations in the sentences.
<h3>DataSet:</h3>DialogSum dataset (115056 Records) was used to fine-tune the model for Punctuation and Capitalization correction.
<h3>Usage:</h3> <pre> from transformers import T5Tokenizer, TFT5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('SJ-Ray/Re-Punctuate') model = TFT5ForConditionalGeneration.from_pretrained('SJ-Ray/Re-Punctuate') input_text = 'the story of this brave brilliant athlete whose very being was questioned so publicly is one that still captures the imagination' inputs = tokenizer.encode("punctuate: " + input_text, return_tensors="tf") result = model.generate(inputs) decoded_output = tokenizer.decode(result[0], skip_special_tokens=True) print(decoded_output) </pre> <h4> Example: </h4> <b>Input:</b> the story of this brave brilliant athlete whose very being was questioned so publicly is one that still captures the imagination <br> <b>Output:</b> The story of this brave, brilliant athlete, whose very being was questioned so publicly, is one that still captures the imagination. <h4> Connect on: <a href="https://www.linkedin.com/in/suraj-kumar-710382a7" target="_blank">LinkedIn : Suraj Kumar</a></h4>- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Mar 16, 2022
- Updated
- Jun 29, 2022