Show HN: Write with Transformer (Trained on ArXiv NLP)
Write With Transformer

		
			This web app, built by the Hugging Face team, is the official demo of the 
			🤗/transformers
			repository's text generation capabilities.
		
		
		
		Models
		
		
			🦄 GPT-2
			
				The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, 
				it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo.
				From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.
			
			
		
		
		
			💯 XLNet
			
				Overcoming the unidirectional limit while maintaining an independent masking algorithm based on permutation, XLNet improves upon the state-of-the-art autoregressive model that is TransformerXL. Using a bidirectional context while keeping its autoregressive approach, this model outperforms BERT on 20 tasks while keeping an impressive generative coherence.
				From the paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding, by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov and Quoc V. Le.
			
			
		
		
		
			☠️ GPT
			
				Released by OpenAI, this seminal architecture has shown that large gains on several NLP tasks can be achieved by generative pre-training a language model 
				on unlabeled text before fine-tuning it on a downstream task.
				From the paper: Improving Language Understanding by Generative Pre-Training, by Alec Radford, Karthik Naraimhan, Tim Salimans and Ilya Sutskever.
			
			
		
		
		
		
		
		
		
		
		
			Do you want to contribute or suggest a new model checkpoint? Open an issue on
			🤗/transformers 🔥.
		
		
			“It is to writing what calculators are to calculus.”
		
	