Fine-Tuning a T5 model for sentence splitting in English
Fine-Tuning a Seq2Seq model for sentence splitting in English.Sentence fusion is the task of dividing a long sentence into multiple sentences.E.g.: the sentence:Mary likes to play football in her freetime whenever she meets with her friends that are very nice people.could be split intoMary likes to play football in her freetime whenever she meets with her friends. Her friends are very nice people.Currently there is only one model on the hub for sentence splitting:huggingface.cogoogle/roberta2roberta_L-24_wikisplit · Hugging FaceWe’re on a journey to advance and democratize artificial intelligence through open source and open science.The goal of this project is to have the best sentence splitting model for English on the hub.ModelOne use one or multiple of the pretrained T5 models:google/byt5-base · Hugging Facegoogle/t5-v1_1-base · Hugging Facet5-base · Hugging FaceDatasetsThe Wikisplit dataset can be used:WikiSplit – Google ResearchAvailable training scriptsAs this will be a Seq2Seq model, therun_summarization_flax.pyscript can be used for training.(Optional) Desired project outcomeThe desired outcome is to have a sentence splitting model for the English language. This can be showcased directly on the hub or with a streamlit  or gradio app.(Optional) ChallengesPreprocessing the dataset can be challenging, but should be feasible. It might even be a nice side-project to add the Wikisplit dataset:WikiSplit – Google Researchto thedatasetslibrary while training the model.ALso it could be difficult to beat the existing model:google/roberta2roberta_L-24_wikisplit · Hugging Face(Optional) Links to read uponhttps://arxiv.org/abs/1907.12461google/roberta2roberta_L-24_wikisplit · Hugging Face
2021-06-25T16:09:22Z
Interested to be part of this project
2021-07-01T03:11:12Z
This is cool project. I am also interested!
2021-07-01T10:02:49Z
Great let’s finalize it2 is enough!
2021-07-05T17:37:54Z
We already have wiki_split dataset added in thedatasetsI think we need to modify this data and make it in seq to seq format