Here are the three pre-trained network series you can use for natural language processing tasks ranging from text classi

In a world where we have easy access to state-of-the-art neural network models, trying to build your own model with limited resources is like trying to reinvent the wheel. It is pointless.

On September 22, 2020, Microsoft announced it had licensed “exclusive” use of GPT-3. Therefore, while others have to rely on the API to receive output, Microsoft has control of the source code. Here is brief info about its size and performance:

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bikini models on Instagram. This is a touchy topic, so let me clarify: It’s not that we’re insecure about our own bodies compared to the women you follow. Hell, most of us don’t even care about that women’s choice of career path. What we care about is respect.

BERT, RoBERTa, DistilBERT, XLNet — which one to use? Google’s BERT and recent transformer-based methods have taken the NLP landscape by a storm, outperforming the…towardsdatascience.com

What’s your formal education and in what field. Is it a well-known institution? For more recent grads, I’ll also look at their GPA and whether they received any excellence awards or honors such as making the Rector’s or Dean’s list. Since Data Science is a wide-open field without any standardized tests or required knowledge, people can enter the field in various methods. In my last blog, I wrote about the 3 main paths taken into the field and based on your education and timing, I’ll figure out which one you probably took. Hence, the timing helps understand your story — how and when did you transition into data science. If you don’t have any formal education in data science, that’s fine, but you need to either demonstrate a track record of work in the field and/or advanced degrees in similar fields.

ELMo, short for Embeddings from Language Models, is a word embedding system for representing words and phrases as vectors. ELMo models the syntax and semantic of words as well as their linguistic context, and it was developed by the Allen Institute for Brain Science. There several variations of ELMo, and the most complex ELMo model (ELMo 5.5B) was trained on a dataset of 5.5B tokens consisting of Wikipedia (1.9B) and all of the monolingual news crawl data from WMT 2008–2012 (3.6B). While both BERT and GPT models are based on transformation networks, ELMo models are based on bi-directional LSTM networks.

BERT stands for Bidirectional Encoder Representations from Transformers, and it is a state-of-the-art machine learning model used for NLP tasks. Jacob Devlin and his colleagues developed BERT at Google in 2018. Devlin and his colleagues trained the BERT on English Wikipedia (2.5B words) and BooksCorpus (0.8B words) and achieved the best accuracies for some of the NLP tasks in 2018. There are two pre-trained general BERT variations: The base model is a 12-layer, 768-hidden, 12-heads, 110M parameter neural network architecture, whereas the large model is a 24-layer, 1024-hidden, 16-heads, 340M parameter neural network architecture. Figure 2 shows the visualization of the BERT network created by Devlin et al.

The West faces a bleak, brutal Covid winter. In America, numbers are exploding all over again — for the third time in a row. Across Europe, cases are falling, but from a massively high peak.

Instead, try to work with these train models, add a couple of new layers on top considering your particular natural language processing task, and train. The results will be much more successful than a model you build from scratch.

And as people congregate for the holidays, cases are likely to rise in the West all over again. In America, they are set to skyrocket to new heights, and in Europe, yet another wave is likely. Meanwhile, in much of the rest of the world — large parts of Asia, Africa — life is slowly returning to some semblance of normality.

Although there are several other pre-trained NLP models available in the market (e.g., GloVe), GPT, BERT, and ELMo are currently the best pre-trained models out there. Since this post aims to introduce these models, we will not have a code-along tutorial. But, I will share several tutorials where we exploit these very advanced pre-trained NLP models.

GPT-3 can create very realistic text, which is sometimes difficult to distinguish from the human-generated text. That’s why the engineers warned of the GPT-3’s potential dangers and called for risk mitigation research. Here is a video about 14 cool apps built on GPT-3:

Even though BERT seems more inferior to GPT-3, the availability of source code to the public makes the model much more popular among developers. You can easily load a BERT variation for your NLP task using the Hugging Face’s Transformers library. Besides, there are several BERT variations, such as original BERT, RoBERTa (by Facebook), DistilBERT, and XLNet. Here is a helpful TDS post on their comparison:

There are three generations of GPT models created by OpenAI. GPT, which stands for Generative Pre-trained Transformers, is an autoregressive language model that uses deep learning to produce human-like text. Currently, the most advanced GPT available is GPT-3; and the most complex version of GPT-3 has over 175 billion parameters. Before the release of GPT-3 in May 2020, the most complex pre-trained NLP model was Microsoft’s Turing NLG.

Instead, try to work with these train models, add a couple of new layers on top considering your particular natural language processing task, and train. The results will be much more successful than a model you build from scratch.