A BRIEF AND EASY EXPLANATION ABOUT TRANSFORMERS AND HUGGING FACE TRANSFORMERS AND THEIR APPLICATIONS IN NATURAL LANGUAGE PROCESSING
Hello guys, Once again, Welcome to our latest blog on “A BRIEF AND EASY EXPLANATION ABOUT TRANSFORMERS AND HUGGING FACE TRANSFORMERS AND THEIR APPLICATIONS IN NATURAL LANGUAGE PROCESSING”.
In this blog, we are going to explain you about What are Transformers and Hugging Face Transformers and their Key features and their applications in Natural Language Processing Tasks, Their working and what are their advantages.
First of all, we should see what is a Transformer in NLP
TRANSFORMERS:
Transformers are one of types of Neural Network Architectures that have been recently developed to use in Artificial Intelligence in their language models.
The Transformer in NLP is a Network architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies and Neural Machine Translations with ease. It includes any tasks that transforms an input sequence into an output sequence.
Some of the examples where the Transformers are used are:
Examples:
Speech Recognition, Text-to-Speech Transformation, etc.
SPEECH RECOGNITION SYSTEM
TEXT TO SPEECH TRANSFORMATION
Now, we will check some of the Key Features of the Transformers.
Here are the some of the Key Features of the Transformers
KEY FEATURES:
a) It is also a deep learning language model.
b) It is mainly used in the fields of Natural Language processing and computer vision.
c) It contains a multi-head self-attention mechanism combined with encoder-decoder structure.
d) Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. So, that it provides the flexibility to use a different framework at each stage of a model’s life.
e) Transformers can process and train more data in lesser time.
f) It can also achieve the State of the Art (SoTA) results easily.
g) They could work with virtually any kind of sequential data.
h) Transformers are the most powerful models ever created in Natural Language Processing.
Here , we will learn about the State of the Art (SoTA)
STATE OF THE ART:
The Meaning of the State of the Art (SoTA) means identification of the prior knowledge to avoid reinventing. It allows to verify or justify that a new knowledge is produced.
Example:
Filing of Patent Rights.
Coming to the Transformers concept, it can achieve SoTA results easily and can perform better than Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN).
First of all, we should understand what are neural networks
NEURAL NETWORKS:
Neural Networks are modelled after inspiring from the structure of neurons in our Brain. Just like Neurons in our brain they connect different areas.
STRUCTURE OF NEURAL NETWORKS
RECURRENT NEURAL NETWORKS (RNN):
RNN is a type of Neural Network where the output from the previous step is fed as input to the current processing step. They are one of the forms of Machine Learning algorithm that are suitable for sequential data such as text, time, financial data, speech, audio, and video.
PROCESSING OF INFORMATION IN RECURRENT NEURAL NETWORKS
RNN has a Memory which stores all information about what has been calculated during processing.
CONVOLUTIONAL NEURAL NETWORKS(CNN):
CNN is a kind of Neural Networks with multiple layers. It processes all the data that has grid like arrangement.
The Big advantage of using CNN is that we have don’t need to do a lot of pre-processing on images.
CONVOLUTIONAL NEURAL NETWORKS (CNN) STRUCTURE
But there is a problem with these models. We will discuss about these problems.
LIMITATIONS AND PROBLEMS:
RNN had problem in word in mapping as mapping words in a language to another language involves sequence of arbitrary length to another sequence of arbitrary length. Encoder-decoder architectures were developed for this type of problem. The decoder only has access to a very reduced representation of the sequence. That means it is only useful for short texts. This is particularly problematic for long texts, as remembering and representing information from far back in the sequence can be lost in the compression to the final representation. As a result, practitioners began to give the decoder access to all of the encoder’s hidden states. This is known as attention.
This is where the name attention comes in, it suggests some way to prioritise which states the encoder is looking at. The clever solution is to assign learnable parameters (or weights, or attention) to each encoder state, at each time step. During training, the decoder learns how much attention to pay to each output at each time step. This process is shown below.
Even this had a problem as sequential computations, requiring inputs to be fed in one at a time, prevents parallelisation across the input sequence. There are a few reasons why this is less than desirable, but one is that it’s slow. To solve this problem, the transformer took another step towards a free-form attention model.
To do this, it removed the recurrent network blocks, and allowed attention to engage with all states in the same layer of the network. This is known as self-attention, and is shown below. Both blocks have self-attention mechanisms, allowing them to look at all states and feed them to a regular neural-network block. This is much faster than the previous attention mechanism (in terms of training) and is the foundation for much of modern NLP practice.
EVOLUTION OF HUGGING FACE TRFANSFORMER:
In order to avoid all the problems facing with previous models and to standardise all the steps involved in training and using a language model, Hugging Face Transformer was founded. They are improving NLP by constructing an API. API in a machine learning can be defined as a remote tool utilizing Machine Learning to solve a specific problem within a specific project with greater accuracy.
This also allows easy access to pretrained models, datasets and tokenising steps. Below, we’ll demonstrate at the highest level of abstraction, with minimal code, how Hugging Face allows any programmer to instantly apply the cutting edge of NLP on their own data.
HUGGING FACE TRANSFORMERS:
Hugging Face is an AI community model and Machine Learning platform created in 2016 by scientists named Julien Chaumond, Clement Delangue, and Thomas Wolf. It aims to democratize NLP by providing Data Scientists, AI practitioners, and Engineers immediate access to over 20,000 pre-trained models based on the state-of-the-art transformer architecture. These models can be applied to both Natural Language Processing and Computer Vision.
Some of the Applications of Hugging Face Transformers are:
APPLICATIONS OF HUGGING FACE TRANSFORMERS:
a) Speech, for tasks such as object audio classification and speech recognition.
b) Text in over 100 languages for performing various tasks such as classification, identification, information extraction, question answering, generation, generation, and translation, recognition.
c) Vision for object detection, ranging, image classification and detection, segmentation.
d) Tabular data for regression and classification problems.
e) Reinforcement Learning transformers.
f) Hugging Face Transformers are Latestly used in Chat GPT, Machine Learning, Artificial Intelligence and Natural Language Processing Applications.
APPLICATIONS OF HUGGING FACE TRANSFORMERS
LIBRARIES IN HUGGING FACE TRANSFORMERS:
Hugging Face Transformers also provides almost 2000 data sets and layered APIs, allowing programmers to easily interact with those models using almost 31 libraries. Most of them are deep learning, such as Pytorch, Tensorflow, Jax, ONNX, Fastai, Stable-Baseline 3, etc.
These courses are a great introduction to using Pytorch and Tensorflow for respectively building deep convolutional neural networks. Other components of the Hugging Face Transformers are the Pipelines.
Let us see about Pytorch and Tensorflow Libraries:
LIBRARIES OF HUGGING FACE TRANSFORMERS
PYTORCH LIBRARY:
Pytorch library is one of the libraries of Machine Learning that is used to power thousands. The main advantage of PyTorch is that it uses Python as the main programming language. Python is undoubtedly the most popular language used for machine learning because of its sheer versatility and ease of use. PyTorch is also fully compatible with popular Python libraries such as SciPy and NumPy.
Pytorch Library is latestly used in ChatGPT application for its functioning.
TENSORFLOW LIBRARY:
TensorFlow is a popular framework of machine learning and deep learning. It is a free and open-source library
It is entirely based on Python programming language and use for numerical computation and data flow, which makes machine learning faster and easier.
TensorFlow can run the deep neural networks for image recognition, handwritten digit classification, recurrent neural network, word embedding, natural language processing, video detection, and many more.
PIPELINES:
They play a key role in HUGGING FACE TRANFORMERS in processing and evaluating the models. Some of the key features of Pipelines are explained below.
1) They provide an easy-to-use API through pipeline() method for performing inference over a variety of tasks.
2) They are used to encapsulate the overall process of every Natural Language Processing task, such as text cleaning, tokenization, embedding, etc.
CONCLUSION:
Transformers Play a huge role in Natural Language Processing tasks. Due, to some limitations in certain models had led to the evolution of Hugging face Transformers. Hugging Face Transformers are playing lead tasks in Artificial Intelligence and Chat-GPT. In future they may take new forms according to the needs of the future technologies.
So, friends We have seen about Transformers and their applications in different technologies and briefly learnt about the Hugging Face Transformers and their usage in different fields and their key features and also learnt about its libraries.
Now, you exactly know what are Transformers and Hugging Face Transformers and also concepts involved in it.
I hope that you all had learnt something new in this blog and I feel it is useful to all you at some point of time in your Data Science career.
All the Best and Please, don’t forget to share your thoughts and opinions in the comment session.