When people say that happiness can only be achieved by being fully in the moment, what this essentially means is making

So, I don’t want to dive deep into BERT since we need a whole different post for that. In fact, I already scheduled a post aimed at comparing rival pre-trained NLP models. But, you will have to wait for a bit.

In any case, being rational human beings we can’t allow our mind to just live with the fact that we failed at achieving that so cherished image. We need to rationalize and we need to move forward. It is just human nature. We therefore reshuffle our self-projection in order to rationalise. It looks something like this:

When people talk about ‘losing their identity’, or having an ‘identity crisis’, effectively they are talking about losing their self-projection. They don’t know what to aim for as what they had thought was going to make them happy turned out not to be so.

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 (2,500M words) and BooksCorpus (800M 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.

It is the constant comparison of our real, current life status to our self-projection that determines our happiness level; the closer I feel to that image, the happier I will feel, and the opposite. But it is only when I finally reach my goals, image, self-projection that I can really assess and confirm (or disconfirm) if that image really led to happiness: was that self-projection genuinely mine? If yes, then I will experience a state of happiness and satisfaction (usually only temporary..); if not, I will experience an identity crisis, until, through the process of rationalization, I will build a new self-projection and start chasing it again.

Natural language processing (NLP) is one of the most cumbersome areas of artificial intelligence when it comes to data preprocessing. Apart from the preprocessing and tokenizing text datasets, it takes a lot of time to train successful NLP models. But today is your lucky day! We will build a sentiment classifier with a pre-trained NLP model: BERT.

Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone.

Situation A: we feel happy as that self-projection was a genuine one, i.e. matching that image that we had set for our self is really what makes us happy. Congrats! You have hit the jackpot! But unfortunately for most people this is not going to last. As you celebrate your happiness your mind is starting already to build a new self-projection and you will soon start chasing it again.

How is it possible that when we finally match the image that for so long we thought was the link to our long-dreamt happiness we feel a sense of hopelessness and disappointment instead?

Very often it can happen that what we thought was our genuine self-projection (i.e. the product of our genuine and true desires) is nothing but the plot society has built for us. As discussed at the beginning: movies, social media, the people we frequent etc. all contribute dramatically to building people’s egos, goals, self-projections. And unfortunately many times as a result we project an image that doesn’t really belong to us and when we reach such image we feel empty, lost and in turn unhappy — just the opposite of what we thought would have happened.

One of the questions that I had the most difficulty resolving was to figure out where to find the BERT model that I can use with TensorFlow. Finally, I discovered Hugging Face’s Transformers library.

Point 4: Our happiness level is positively impacted by anticipating fulfilling the self-projection we have in our mind until reality hits and at that point there’s no more room for anticipation. Most of the time we rationalize, reshuffle our self-projection and start chasing it again.

Ouch! This hurts. You probably spent many years fighting and hoping to finally be that perfect image you had envisioned for so long in your head only to finally realize it is just not going to happen. Maybe you were not lucky enough. Or maybe you just set an overly optimistic self-projection. In fact, human beings are most of the time inherently unhappy not because of what happens to them, but because of their tendency to project oftentimes unrealistic and excessively optimistic images of themselves in the future.

Additionally, I believe I should mention that although Open AI’s GPT3 outperforms BERT, the limited access to GPT3 forces us to use BERT. But rest assured, BERT is also an excellent NLP model. Here is a basic visual network comparison among rival NLP models: BERT, GPT, and ELMo:

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ady to be done with Intel after years of being unable to build the Mac it really wanted, due to the lagging production schedules of the Intel Core processors it was using. To that end, Apple made its point. The M1 isn’t just a little bit faster or more efficient, it’s significantly so. So much, in fact, that it’s a little embarrassing.