ALBERTGoogle
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About
ALBERT is a self-supervised Transformer model that was pretrained on a large corpus of English data. This means it does not require manual labelling, and instead uses an automated process to generate inputs and labels from raw texts. It is trained with two distinct objectives in mind. The first is Masked Language Modeling (MLM), which randomly masks 15% of words in the input sentence and requires the model to predict them. This technique differs from RNNs and autoregressive models like GPT as it allows the model to learn bidirectional sentence representations. The second objective is Sentence Ordering Prediction (SOP), which entails predicting the ordering of two consecutive segments of text during pretraining.
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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
Windows
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Cloud
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iPhone
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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
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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
No information available.
Free Version
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Pricing
Free
Free Version
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Reviews/
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Training
Documentation
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Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
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Live Online
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Company InformationGoogle
Founded: 1998
United States
github.com/google-research/albert
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Company InformationXLNet
Founded: 2019
github.com/zihangdai/xlnet
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