Archives for language modelling
“As a successor of LSTM. We have a new thing. It's not published, it's hidden. It's called XLSTM,” says the German computer scientist Sepp Hochreiter
The post Sepp Hochreiter’s Quest to Kick OpenAI from Language Modelling Supermarket appeared first on Analytics India Magazine.
Entity Constrained Insertion Transformer, shortly known as the ENCONTER, introduces entity constraints to control text generation efficiently
The post Complete Guide to ENCONTER: Entity Constrained Insertion Transformer for Language Modeling appeared first on Analytics India Magazine.
Entity Constrained Insertion Transformer, shortly known as the ENCONTER, introduces entity constraints to control text generation efficiently
The post Complete Guide to ENCONTER: Entity Constrained Insertion Transformer for Language Modeling appeared first on Analytics India Magazine.
DeLighT is a deep and light-weight transformer that distributes parameters efficiently among transformer blocks and layers
The post Complete Guide to DeLighT: Deep and Light-weight Transformer appeared first on Analytics India Magazine.
Researchers from Salesforce have released a new powerful generative language model that reaches a new milestone in generative language models’ history!
The post Guide to Salesforce’s CTRL: Conditional Transformer Language Model appeared first on Analytics India Magazine.
XLnet is an extension of the Transformer-XL model. It learns bidirectional contexts using an autoregressive method. Let’s first understand the shortcomings of the BERT model so that we can better understand the XLNet Architecture. Let’s see how BERT learns from data.
The post Guide to XLNet for Language Understanding appeared first on Analytics India Magazine.
XLnet is an extension of the Transformer-XL model. It learns bidirectional contexts using an autoregressive method. Let’s first understand the shortcomings of the BERT model so that we can better understand the XLNet Architecture. Let’s see how BERT learns from data.
The post Guide to XLNet for Language Understanding appeared first on Analytics India Magazine.
In recent times, Language Modelling has gained momentum in the field of Natural Language Processing. So, it is essential for us to think of new models and strategies for quicker and better preparation of language models. Nonetheless, because of the complexity of language, we have to deal with some of the problems in the dataset. With an increase in the size of the dataset, there is an increase in the normal number of times a word shows up in that dataset.
The post Datasets for Language Modelling in NLP using TensorFlow and PyTorch appeared first on Analytics India Magazine.
Language modelling is the speciality of deciding the likelihood of a succession of words. These are useful in many different Natural Language Processing applications like Machine translator, Speech recognition, Optical character recognition and many more.In recent times language models depend on neural networks, they anticipate precisely a word in a sentence dependent on encompassing words. However, in this project, we will discuss the most classic of language models: the n-gram models.
The post Complete Guide on Language Modelling: Unigram Using Python appeared first on Analytics India Magazine.
With an aim to break down language barriers across the globe for everyone to understand and communicate with anyone, the researchers at Facebook AI Research (FAIR) work on complex problems to deploy robust language translation solutions. It spans the topics such as deep learning, natural language processing, text normalisation, word sense disambiguation and much more. …
The post Facebook Explains How Nearest Neighbour Search Is An Effective Approach For Language Modelling In The Long Tail appeared first on Analytics India Magazine.