Natural language processing (NLP) is one of the most fascinating topics in AI, and it has already spawned technologies such as chatbots, voice assistants, translators, and a slew of other everyday utilities.
Natural language processing research began in the 1950s, with the earliest attempts at automated translation from Russian to English establishing the foundation for future research. Around the same time, the Turing Test, also known as the imitation game, was designed to see if a machine could behave like a human.
Like most of the time, we humans confuse in understanding what the other person is trying to say, but NLP has made this complex task much easier for machines.
There are a few NLP segments that are designed for a few datasets that we call instructed data. They are called NLP models. In this blog, we are going to have a look at the top NLP models out there. As we dive in, let us have a look at what NLP models are
What are Pre-Trained models for NLP?
Deep learning models that have been trained on a large dataset to accomplish certain NLP tasks are known as pre-trained models (PTMs) for NLP. When PTMs are trained on a large corpus, they can acquire universal language representations, which can help with downstream NLP tasks and prevent having to train a new model from scratch.
Pre-trained models can thus be referred to as reusable NLP models, which NLP developers can employ to quickly construct an NLP application. Transformers offers a collection of pre-trained deep learning NLP models for a variety of NLP applications, including text classification, question answering, machine translation, and more.
These pre-trained NLP tasks are free to use and do not require any prior knowledge of NLP. Pre-trained models of the first generation were taught to learn good word embeddings.
NLP models can be simply loaded into NLP libraries like PyTorch, Tensorflow, and others and utilized to perform NLP tasks with little effort on the part of NLP developers. Pre-trained models are increasingly being employed on NLP jobs since they are simpler to install, have higher accuracy, and require less training time than custom-built models.
Top NLP Models
BERT is a pre-trained model that uses both the left and right sides of a word to determine its context. BERT heralds a new age in NLP because, despite its precision, it is built on two simple concepts.
Pre-training and fine-tuning are the two key processes in BERT. BERT is trained on unlabeled data with multiple training challenges in the first step of the model. This is accomplished by carrying out two unattended tasks:
To avoid cycles where the word being processed can see itself, a deep bidirectional model is randomly trained by covering — masking — some input tokens.
Every pre-train set is used 50% of the time in this challenge. When a sentence S1 is followed by a sentence S2, S2 is classified as IsNext. S2, on the other hand, will be labeled NotNext if S2 is a random sentence.
After it is completed, fine-tuning can begin. Using labeled data, all of the model's parameters are improved at this step. "Downstream tasks" provided this labeled data. Each downstream job is a unique model with its own set of parameters.
BERT can be used for a variety of tasks, including named entity recognition and question-answering. TensorFlow or PyTorch are two tools that can be used to implement the BERT model. (here)
GPT-3 is a transformer-based NLP model that can translate, answer questions, compose poetry, solve clozes, and execute tasks that require on-the-fly reasoning, such as unscrambling words. The GPT-3 is also used to compose news stories and develop codes, thanks to recent advancements.
GPT-3 is capable of handling statistical interdependence between words. It's been trained on over 175 billion parameters and 45 TB of text gathered from all over the web. It is one of the most comprehensive pre-trained NLP models accessible.
GPT-3 is unique among language models in that it does not require fine-tuning to complete downstream tasks. Developers can reprogram the model using instructions thanks to its 'text in, text out' API.
XLNet was created by a team of Google and Carnegie Mellon University academics. It was created to deal with standard natural language processing tasks including sentiment analysis and text classification.
XLNet is a pre-trained generalised autoregressive model that combines the greatest features of Transformer-XL and BERT. XLNet makes use of Transformer-autoregressive XL's language model and BERT's autoencoding.
The key benefit of XLNet is that it was created to combine the best features of Transformer-XL and BERT without the drawbacks.
Bidirectional context analysis is at the heart of XLNet, just as it is in BERT. This implies it considers both the words preceding and after the token being analysed in order to guess what it might be. XLNet goes beyond that and calculates the log-likelihood of a sequence of words concerning its possible permutations.
XLNet circumvents BERT's drawbacks. Because it is an autoregressive model, it is not affected by data corruption. Experiments have shown that XLNet outperforms both BERT and Transformer-XL in terms of performance.
If you wish to use XLNet in your next project, the researchers behind it have provided an official Tensorflow implementation. A PyTorch implementation of XLNet is also available.
To know more about XLNet, watch this:
RoBERTa is a natural language processing model that is constructed on top of BERT in order to improve its performance and overcome some of its flaws. RoBERTa was created as a consequence of collaboration between Facebook AI and the University of Washington.
The research team examined the performance of bidirectional context analysis and discovered various adjustments that may be made to improve BERT's performance, such as using a larger, new dataset for training the model and eliminating the following sentence prediction.
RoBERTa, which stands for Robustly Optimized BERT Approach, is the consequence of these changes. The following are the differences between BERT and RoBERTa:
A larger training dataset of 160GB is available.
The increased dataset and 500K iterations resulted in a longer training time.
The model's next sentence prediction portion has been removed.
Changing the LM masking algorithm used on the training data.
The RoBERTa implementation was released as open-source on Github as part of the PyTorch package.
Another BERT-modified model is ALBERT. When utilizing BERT, Google researchers discovered that the size of the pre-trained dataset grew larger, affecting both the memory and time required to execute the model.
To address these drawbacks, Google researchers developed ALBERT, a lighter version of BERT. The ALBERT provides two ways for dealing with BERT's memory and timing concerns. This is accomplished by factoring in the embedded parameterization and sharing it across layers.
Furthermore, instead of establishing this in the pre-training phase, ALBERT uses a self-supervised loss to conduct the next sentence prediction. This step is necessary to get around BERT's inter-sentence coherence constraints.
The original codebase developed by Google may be found on the Google research repository on Github if you want to try out ALBERT. Both TensorFlow and PyTorch can be used with the ALBERT implementation.
In the last, the value and benefits of pre-trained language models are obvious. Thankfully, developers have access to these models, which enable them to produce precise results while saving resources and time during the creation of AI applications.
Which NLP language model, on the other hand, is best for your AI project? Well, that depends on the project's scope, dataset type, training approaches, and a variety of other things.