Top 8 SOTA (State of the Art) pre-trained NLP Models for data scientists

July 6, 2022

Natural language processing or NLP is an artificial intelligence (AI) branch which deals with computer-human interaction using the natural way we use language. The ultimate aim of NLP is to read, decode, comprehend and take useful information out of human languages and understand the logic in them.

NLP uses linguistics, computer science and AI and then analyses a huge amount of natural language data. With the help of NLP, programmers have developed ML applications to train models using existing Python frameworks to use language for various purposes.

Why is NLP necessary?

NLP is related to many language-related tasks. It can help in –

  • Tools for monitoring social media
  • Spam filters
  • Search engines
  • Voice assistants
  • Grammar correction
  • Translation
  • Text-to-speech converter and vice versa
  • Plagiarism detection
  • Chatbots

In this blog, we will discuss the state-of-the-art software that has been developed by leading players in the field of NLP.

Top 8 NLP Models for Data Scientists-

Following are 8 NLP models most used by data scientists –

  1. Facebook RoBERTa

Facebook’s RoBERTa or Robustly Optimised BERT has been built on the masking strategy of BERT. It is an optimised method for the NLP pre-training system that is self-supervised. It can be used for -  

  • Predicting sections of text that have been hidden intentionally
  • It is being trained to process data from news articles so it can block fake news or other provocative text
  1. ULMFiT

Universal Language Model Fine-tuning or ULMFiT is used to perform many NLP tasks. It reduces the scope of error by 18-24%. Sebastian Ruder and Jeremy Howard developed it and you can use it for –

  • Processing text
  • Converting voice to text and vice versa
  • Understanding the context of the textual language
  1. Google ALBERT

Google ALBERT is an upgraded form of BERT. The model is an open-source application on the TensorFlow framework. It has only 12 million parameters. It has approximately 80.1% accuracy. Google ALBERT is used for –

  • Abstract summarisation
  • Sentence prediction
  • Question answering
  • Conversational response generation

Google ALBERT does all the tasks better than BERT. The accuracy is around 80-83%.

  1. XLNet

XLNet is a Transformer-XL model extension that Google develops. It is a pre-trained NLP used to learn the functions from contexts in two directions. It is used to perform NLP tasks like –

  • Answering questions
  • Text classification
  • Analysing sentiments

In language processing tasks, XLNet has even outperformed BERT.

  1. ELMo

ELMo is the abbreviated form of Embeddings from Language Models. It analyses and trains models on the syntax and semantics of words. It also understands their contexts linguistically. This model was developed by Allen NLP on a large amount of text and learned functions from biLM or deep bi-directional models. It can perform –

  • Textual entailment
  • Sentiment analysis
  • Answering questions
  1. Microsoft CodeBERT

Microsoft’s CodeBERT is an NLP framework that is built upon a multi-layer bi-directional neural architecture. It can perform tasks like –

  • Code documentation generation
  • Code search

Microsoft CodeBERT has also been trained on a dataset that is the largest of repositories from Github in six programming languages.

  1. Google BERT

BERT’s full form is Bidirectional Encoder Representations from Transformers. Alphabet or Google developed this pre-trained NLP model in 2018. It allows anybody to train a model that can answer questions on its own. This task can be completed in nearly 30 minutes either on a single cloud Tensor processing unit. It can do the same in a few hours on a single graphics processing unit. BERT has achieved a precision of 93.2%, which is reportedly the highest as of now. BERT can be used for language generation tasks that are sequence-to-sequence based like -

  • Abstract summarisation
  • Conversational response generation
  • Sentence prediction
  • Question answering
  1. Open AI-GPT3

Open AI-GPT-3 is a pre-trained NLP model which OpenAI has developed. The features of this NLP are –

  • It is a large-scale NLP that is transformer based
  • The NLP has been pre-trained on 175 billion parameters
  • It can perform tasks like –
  • Answering questions
  • Translation
  • Special tasks which require logical reasoning on the go such as unscrambling words
  • Write news articles 
  • Generate codes for assisting developers in building ML applications

By and Large-

To sum up, the advancement in the field of data science has led to the widespread usage of such NLP models. Resultantly, the upgradation of such models has made them simpler to use for everyone in this field. However, there are other NLP models available that can help, but these aforementioned eight are some of the popular ones among data scientists.

Reference Links-

https://analyticsindiamag.com/top-8-pre-trained-nlp-models-developers-must-know/ 

https://www.topbots.com/leading-nlp-language-models-2020/ 

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

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  • Training used

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  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

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  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

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So, read on to know more.

What is Deep Q-Learning?

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State> Next state> Action> Reward

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What is Reinforcement Learning?

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Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

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Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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