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 po

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.

Now we have our basic train and test datasets, I want to prepare them for our BERT model. To make it more comprehensible, I will create a pandas dataframe from our TensorFlow dataset object. The following code converts our train Dataset object to train pandas dataframe:

Saudi Arabia invested billions of dollars into Uber. Is there a certain point where Silicon Valley will throw up its hands and say, “You guys got a lot of money, but we don’t really want to do business with you anymore?”

And as far as I know, they never found anything anywhere. So it just sounds like he downloaded something and then he threw it away or never used it. This only came up years later, or a year later since the acquisition was done. So that’s my only guess as to motivation.

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:

The only thing I could speculate on is that Google owed him a whole bunch of money from some acquisition that they did, and maybe he wasn’t sure he was going to get it. This was some way of making sure he had something to hold them accountable to that. But Uber didn’t need their stuff. Their stuff was irrelevant to us. Uber was building something totally different. We wanted Anthony’s brain, but we didn’t want his work product or Google’s work product.

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l like another baton has been passed — for years, NSF had been seeking private investment to keep the facility running, as it had long fallen into a state of disrepair. Now, the ascendent lights in space exploration are fewer public institutions and more private corporations like SpaceX — designed to serve the bottom line and interests of a CEO rather than a public.

Let’s talk about Saudi Arabia. U.S. intelligence agencies say the country intended to kill a U.S. journalist, Jamal Khashoggi. We know that the FBI also accused people close to the Saudi royal family of trying to log into Twitter systems and get data from dissidents.

We have two pandas Dataframe objects waiting for us to convert them into suitable objects for the BERT model. We will take advantage of the InputExample function that helps us to create sequences from our dataset. The InputExample function can be called as follows:

Now that we have our data cleaned and prepared, we can create text_dataset_from_directory with the following lines. I want to process the entire data in a single batch. That’s why I selected a very large batch size:

4 Reasons Why You Should Use Google Colab for Your Next Project Learn whether iPython, Jupyter Notebook, and Google Colab are Rivals or Complimentary Tools; Understand Their…towardsdatascience.com

2 — convert_examples_to_tf_dataset: This function will tokenize the InputExample objects, then create the required input format with the tokenized objects, finally, create an input dataset that we can feed to the model.

IMDB Reviews Dataset is a large movie review dataset collected and prepared by Andrew L. Maas from the popular movie rating service, IMDB. The IMDB Reviews dataset is used for binary sentiment classification, whether a review is positive or negative. It contains 25,000 movie reviews for training and 25,000 for testing. All these 50,000 reviews are labeled data that may be used for supervised deep learning. Besides, there is an additional 50,000 unlabeled reviews that we will not use in this case study. In this case study, we will only use the training dataset.

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.

After the installation is completed, we will load the pre-trained BERT Tokenizer and Sequence Classifier as well as InputExample and InputFeatures. Then, we will build our model with the Sequence Classifier and our tokenizer with BERT’s Tokenizer.