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Description
We introduce T5, a model that transforms all natural language processing tasks into a consistent text-to-text format, ensuring that both inputs and outputs are text strings, unlike BERT-style models which are limited to providing either a class label or a segment of the input text. This innovative text-to-text approach enables us to utilize the same model architecture, loss function, and hyperparameter settings across various NLP tasks such as machine translation, document summarization, question answering, and classification, including sentiment analysis. Furthermore, T5's versatility extends to regression tasks, where it can be trained to output the textual form of a number rather than the number itself, showcasing its adaptability. This unified framework greatly simplifies the handling of diverse NLP challenges, promoting efficiency and consistency in model training and application.
Description
XLNet introduces an innovative approach to unsupervised language representation learning by utilizing a unique generalized permutation language modeling objective. Furthermore, it leverages the Transformer-XL architecture, which proves to be highly effective in handling language tasks that require processing of extended contexts. As a result, XLNet sets new benchmarks with its state-of-the-art (SOTA) performance across multiple downstream language applications, such as question answering, natural language inference, sentiment analysis, and document ranking. This makes XLNet a significant advancement in the field of natural language processing.
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Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
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Live Rep (24/7)
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Training Docs
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Live Training (Online)
In Person
Vendor Details
Company Name
Founded
1998
Country
United States
Website
ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html
Vendor Details
Company Name
XLNet
Founded
2019
Website
github.com/zihangdai/xlnet