Introduction to NLP systems and pre-trained models
What is an NLP system?
What is Natural language processing (NLP)? Well, for starters, it’s something you are doing right now. Yes, as you read the words and sentences of this article, you form some comprehension from it. And when we program a computer to do this, we form an NLP or natural language processing system.
NLP systems start with something known as unstructured text. What is that? Well, that’s just some form of communication.
The job of an NLP system is to translate two types of texts – Structured and Unstructured.
Some use cases of NLP systems include:
What is meant by a pre-trained model?
A pre-trained model is a deep learning model trained on large datasets to perform the tasks that an NLP normally would perform.
Reusable NLP models are especially handy for developers, who can now use them to build NLP applications quickly.
Pretrained models have an easy implementation, and high accuracy, and require little to no training time compared to custom-built models.
Speech Recognition is a common example of an NLP task where a pre-trained model may be used.
NLP pre-trained models for speech recognition are employed in many NLP APIs available online, for instance, Amazon Alexa, Google API, etc.
What is RoBERTa?
To understand what RoBERTa is, you must first familiarize yourself with BERT.
What is BERT?
BERT is developed to assist computers in understanding the definition of ambiguous language in the text by using surrounding text to appoint context. It is an open-source machine learning framework for NLP.
The framework BERT was developed on was pre-trained using text from Wikipedia and can be made more accurate with the help of question and answer datasets.
Bidirectional Encoder Representations from Transformers (BERT) is established on Transformers, a deep learning model in which every output element is linked to every input element, and the weightings between them are dynamically calculated based on their connection. (This is called attention in NLP)
Most language models can only read text input sequentially, either left to right or right to left but couldn’t do both simultaneously. However, BERT is different because it is designed to read simultaneously in both directions. This ability, enabled by the introduction of Transformers, is what the B in BERT represents (bi-directionality).
Using this bidirectional ability, BERT is pre-trained on two different but related NLP tasks:
- Masked Language Modeling
Masked Language Model training aims to train the model to predict the next word in a sentence. This is achieved by hiding a word in a sentence and then having the program predict what word has been hidden (masked) based on the masked word’s context. The image below shows an example of MLM.
- Next Sentence Prediction
Next Sentence Prediction training aims to train the model to predict whether two given sentences have a rational, sequential relationship or whether their relationship is just random.
How is RoBERTa different from BERT?
RoBERTa is an acronym for Robustly Optimized Bidirectional Encoder Representations from Transformers (BERT) pre-trained Approach. It is a model that improves on BERT. Experimenters introduced it at Facebook and Washington University.
RoBERTa is different and possibly better than BERT due to the following reasons:
- Replaced static masking
BERT uses static masking. The part of the sentence is masked in each Epoch. In contrast, RoBERTa uses dynamic masking, in which different parts of the sentences are masked for different Epochs—making the model more robust.
- Removed Next sentence Prediction (NSP) Task
NSP task did prove to be very useful while pre-training the BERT model. Therefore, the RoBERTa only utilizes the Masked Language Modeling (MLM) task.
- More DataPoints
BERT is pre-trained on BooksCorpus and English Wikipedia, which only has 16 GigaBytes of data. While RoBERTa is pre-trained onBooksCorpus, CC-News, OpenWebText, and Stories which has a total of 160 GigaBytes of data.
- Large Batch size
Using a larger batch size improved the model’s speed and performance RoBERTa used a batch size of 8,000 with 300,000 steps. BERT utilizes a batch size of 256 with 1 million steps.
- Replaced Word-piece tokenization
Instead of Word-piece tokenization, RoBERTa uses Character-level byte-pair encoding.
Algoscale provides its clients with the best.
Algoscale’s top priority is to provide its clients with the best services. We have been using this model to build NLP applications so that you get user-friendly and interactive NLP applications.
Developers at Alogoscale use the RoBERTa model instead of the BERT model because of its robust and efficient nature.
We hope this blog helped you understand the work and idea behind RoBERTa. You can visit our services page to learn more about our services.