“Artificial Intelligence and Natural Language Processing may well make the internet far more useful than it is today.”- Vint Cerf
Initiating in the 1950s as the cloverleaf of AI and linguistics, NLP comes in the picture. Incipiently, NLP was dissimilar from text information retrieval (IR), in order to employ large-scalable statistics-based techniques for indexing and searching massive volume of text adroitly.
In this blog, we will discuss SqueezeBERT, the newest technique which is known for delivering agile mobile NLP, that also keeps a high level of accuracy in comparison to other BERT, plus its specification and architecture.
Most NLP research is planted on advance Transformer-based networks in recent years. Because of the availability of the massive data that augments extensively, extended computing system and advance neural network models to operate that data, Natural language processing technology has done vital strides in order to understand, proofread, and managing this data.
Further, plausible chances are there for exploiting NLP in multiple applications for assisting web users, social thread, and business domains. (Visit our section, to discover more about more NLP)
People write more than 300 billion messages every day, out of which half of the emails are read on mobile phones and half of the Facebook users access Facebook in mobile devices. On the other hand, NLP has the potential for helping users in various ways.
When an individual writes a text, spelling, grammar inspecting and sentence conclusion can be done with the help of NLP models. Moreover;
When the content is appended to a social network, Natural Language Processing could expedite content alteration earlier when it appears at news feed of other users.
Also, when a person uses up text, NLP models assist in classifying text into folders, framing news feeds, computing texts, recognizing mimic texts, etc. (Related blog: Introduction to Text Analytics and Models in Natural Language Processing)
Although from the past few years, deploying advance Attention Neural Networks lead to enhancement in every filed of NLP.
In reference to the research paper that details SqueezeBERT, a mobile NLP neural network architecture, is 4.3 times in comparison to BERT on a 3 Pixel smart mobile device and at the same time attaining the accuracy identical to MobileBERT in GLUE benchmark tasks.
However, presently high-scalable NLP neural network models like BERT and RoBERTa are notably computationally expensive, alongside BERT-depended taking 1.7 seconds for categorizing a text snippet on the 3 Pixel smart mobile device.
Holding the antithesis amid SqueezeBERT and MobileBERT is “the implementation of assorted convolution to upgrade the speed and efficiency”!!!!!
(Learn 7 natural language processing techniques for extracting information, click here)
SqueezeBERT exhibits the specialties;
SqueezeBERT depends on the procedures acquired from SqueezNAS, a neural architecture search (NAS) model.
SqueezeBERT executes on lower latency on a smart mobile device (Google Pixel 3 smartphone) related to BERT-base, MobileBERT, and various useful NLP models, though sustaining efficiency.
The Architecture of SqueezeBERT
Conventionally, the BERT-derived networks involve 3 stages basically;
Embedding, to transfer preprocessed words (interpreted as integer-valued tokens) into abstracted feature-vectors of floating-point numbers;
The encoder that is consisted of a set of self-attention and other layers, and
The classifier that makes the final outcome of the network.
The proposed neural architecture (known as SqueezeBERT) practices assorted convolutions, it is identical to BERT-base, but the PFC layer deployed as convolutions and assorted convolutions for various layers. It consists of an encoder that has a self-attention module with 3 PFC layers, and more 3 PFC layers termed as the feed-forward network layers in terms of FFN1, FFN2, and FFN3 where all the layers incorporate various dimensions.
For pretraining, an aggregate of the Wikipedia and Corpus of Books is practiced, placing down 3% of the consolidated dataset in the form of a test dataset. Masked Language Modelling (MLM) is adopted and Sentence Order Prediction(SOP) in the form of pretraining tasks.
Finetuning and assessing SqueezeBERT, even more baselines, are conducted on the General Language Understanding Evaluation(GLUE) set of tasks. This consolidated benchmark incorporates a huge variety of nine NLU tasks.
GLUE has marked as the standard evaluation benchmark for most of the NLP research.
The efficiency of the model over the GLUE tasks is highly likely to deliver an excellent estimation of the generalizability of that model, particularly to some text classification tasks.
Most of the latest research over adequate NLP networks proclaims outcomes on the basis of models that are trained with bells and whistles including distillation, adversarial training, transfer learning over GLUE tasks, etc.
There are no such parameters or generalizations of training strategies over several papers that are making it hard to differentiate the benefaction of the model from the participation of the training approaches to the conclusive accuracy number.
So, SqueezeBERT gets first trained by deploying an easy training proposal and then trained with distillation and other applicable techniques.
A famous technique “grouped convolutions” while designing significant computer vision neural networks, is applied to NLP. SqueezeBERT that is an efficient NLP model that uses mostly layers of self-attention encoder along with one dimensional grouped convolutions.
"Computers are incredibly fast, accurate and stupid; humans are incredibly slow, inaccurate and brilliant; together they are powerful beyond imagination." --Albert Einstein
SqueezeBERT runs on almost 4 times lower latency than BERT-base when standardized on Google Pixel 3 smartphone. Even it has the potential to obtain achieve comparable with a distillation-trained MobileBERT and native version of BERT-base.