T5Google
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About
With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself.
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About
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking.
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Platforms Supported
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Cloud
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
AI developers interested in a powerful large language model
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Audience
Developers interested in a solution for generalized autoregressive pretraining for language understanding
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Support
Phone Support
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24/7 Live Support
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Online
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Support
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API
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Pricing
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Free Version
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Pricing
Free
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Training
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Training
Documentation
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Webinars
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Company InformationGoogle
Founded: 1998
United States
ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html
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Company InformationXLNet
Founded: 2019
github.com/zihangdai/xlnet
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