What is XLNet used for?

XLNet is a large bidirectional transformer that uses improved training methodology, larger data and more computational power to achieve better than BERT prediction metrics on 20 language tasks. To improve the training, XLNet introduces permutation language modeling, where all tokens are predicted but in random order.

How is XLNet trained?

XLNet is a method of learning language representation using the generalized autoregressive pretraining method. It’s objective is to learn the language model. It has been trained on a large corpus using the permutation language modeling objective. It has surpassed BERT on various NLP tasks.

Is XLNet a transformer?

Besides, XLNet is based on the Transformer-XL which it uses as the main pretraining framework. It adopts the method like segment recurrent mechanism and relative encoding from Transformer-XL model.

Is XLNet a language model?

XLNet is the latest and greatest model to emerge from the booming field of Natural Language Processing (NLP). XLNet is an auto-regressive language model which outputs the joint probability of a sequence of tokens based on the transformer architecture with recurrence.

Who created XLNet?

XLNet, a new model by people from CMU and Google outperforms BERT on 20 tasks.” – Sebastian Ruder, research scientist at Deepmind. “XLNet will probably be an important tool for any NLP practitioner for a while… [it is] the latest cutting-edge technique in NLP.” – Keita Kurita, Carnegie Mellon University.

Is XLNet an encoder or decoder?

Transformer Architecture It basically revolves around “attention”. It is an encoder-decoder model where you map one sequence to another — English to French.

What Can You Do With topic modeling?

Topic Models are very useful for the purpose for document clustering, organizing large blocks of textual data, information retrieval from unstructured text and feature selection. For Example – New York Times are using topic models to boost their user – article recommendation engines.

Is Topic Modelling useful?

Topic modelling provides us with methods to organize, understand and summarize large collections of textual information. It helps in: Discovering hidden topical patterns that are present across the collection.