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# Personalized Text Generation with Fine-Grained Linguistic Control
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This model was obtained by fine-tuning the [Pythia-1b](https://huggingface.co/EleutherAI/) model on three datasets: [Blogs Authorship Corpus](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm), [IMDb62](https://umlt.infotech.monash.edu/?page_id=266), and [Amazon 5-core Reviews](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/).
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The model was built so that it can generate text based on multiple fine-grained linguistic attributes that reflect authors’ writing style. Our fine-tuning procedure and the hyperparameters we used can be found in our paper "*[Personalized Text Generation with Fine-Grained Linguistic Control](https://arxiv.org/abs/2402.04914)*" Our fine-tuning code and data can be found [here](https://github.com/balhafni/personalized-gen).
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## Usage
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# Personalized Text Generation with Fine-Grained Linguistic Control
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This model was obtained by fine-tuning the [Pythia-1b](https://huggingface.co/EleutherAI/) model on three datasets: [Blogs Authorship Corpus](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm), [IMDb62](https://umlt.infotech.monash.edu/?page_id=266), and [Amazon 5-core Reviews](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/).
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The model was built so that it can generate text based on multiple fine-grained linguistic attributes that reflect authors’ writing style. Our fine-tuning procedure and the hyperparameters we used can be found in our paper "*[Personalized Text Generation with Fine-Grained Linguistic Control](https://arxiv.org/abs/2402.04914)*". Our fine-tuning code and data can be found [here](https://github.com/balhafni/personalized-gen).
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## Usage
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