To use a pre-trained BERT model, we need to convert the input data into an appropriate format so that each sentence can be sent to the pre-trained model to obtain the corresponding embedding. This article introduces how this can be done using modules and functions available in Hugging Face’s transformers package (https://huggingface.co/transformers/index.html).

PyCon HK 2018 was held on 23-24th November 2018 at Cyberport. I gave a talk on how to deploy machine learning models in Python. The slides of the talk can be found at the link: http://talks.albertauyeung.com/pycon2018-deploy-ml-models/.

PyCon HK 2017 was held on 3rd-4th November 2017 at the City University of Hong Kong. I gave a talk on using gradient boosting machines in Python to perform machine learning. The slides of the talk can be found at the link: http://talks.albertauyeung.com/pycon2017-gradient-boosting/.

In natural language processing, it is a common task to extract words or phrases of particular types from a given sentence or paragraph. For example, when performing analysis of a corpus of news articles, we may want to know which countries are mentioned in the articles, and how many articles are related to each of these countries.

There is probably no need to say that there is too much information on the Web nowadays. Search engines help us a little bit. What is better is to have something interesting recommended to us automatically without asking. Indeed, from as simple as a list of the most popular questions and answers on Quora to some more personalized recommendations we received on Amazon, we are usually offered recommendations on the Web.