Improving the performance of machine learning models by enhancing their most essential key.

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Photo by Mert Kahveci on Unsplash

Have you ever felt that you have tried many machine learning methods and their hyperparameter configurations but still haven’t found any good results? This might be something for you!

Actually, there are many methods you can use to improve the performance of your model, from choosing the state-of-the-art methods, increasing the number of epochs, changing the batch size, choosing the right optimizer, and many more. However, the first thing that comes to mind before creating a machine learning model is data. …

Important advice about machine learning from development to production

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Photo by Frame Harirak on Unsplash

Working in a startup company mostly is about projects. Tight deadlines, small teams, rapidly-changing requirements and a lot of others. Unfortunately, my very first project as a machine learning engineer was not so beginner-friendly. It was a big project, with a big client, a big responsibility, and… a small team. And one of those team members was me, a newbie.

Recalling the day when the project leader had me work on a dozen models for this project. It frustrated me even just thinking about that. …

From predicting single sentence to fine-tuning using custom dataset to finding the best hyperparameter configuration.

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Photo by Jamie Street on Unsplash

BERT has become a new standard for Natural Language Processing (NLP). It achieved a whole new state-of-the-art on eleven NLP task, including text classification, sequence labeling, question answering, and many more. Even better, it can also give incredible results using only a small amount of data. BERT was first released in 2018 by Google along with its paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Now we can easily apply BERT to our model by using Huggingface (🤗) Transformers library. The library already provided complete documentation about other transformers models too. You can check it here. In this post, I will try to summarize some important points which we will likely use frequently. We will take a look at how to use and train models using BERT from 🤗 Transformers. Later, you can also utilize other transformers models (such as XLM, RoBERTa, XLM RoBERTa (my favorite!), …

Getting Started

A complete introduction to the next level of sentiment analysis.

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Sentiment analysis has been widely used by several types of industries for the last decades. Not only it can produce helpful insights, but also save time and energy by leveraging the power of machine learning rather than manually gathering and analyzing the information from a bunch of data. It simply classifies whether an input (usually in the form of sentence or document) contains positive or negative opinion. One simple example below clearly indicates that the sentiment is negative.


Arfinda Ilmania

NLP engineer. A self-taught learner.

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