Category: Machine Learning

Tsuyoshi Matsuzaki tries out a named entity recognition project with the Hugging Face library:

Now a lot of AI companies (such as, OpenAI, NLP Cloud, Google, NVIDIA, etc) are providing pre-trained large language models including methods that tune to enable models trained. Among such tools and framework, HuggingFace is widely used and providing over 20,000 transformer-based models.

In this post, I’ll show you brief fine-tuned example of transformer models in Hugging Face for your beginning.
In the last part of this post, I’ll also optimize training with DeepSpeed which is well integrated with HuggingFace transformers.

Click through for the results of this analysis.

Stefania Cristina continues a series on transformer models. First up is plotting loss curves:

We have previously seen how to train the Transformer model for neural machine translation. Before moving on to inferencing the trained model, let us first explore how to modify the training code slightly, in order to be able to plot the training and validation loss curves that can be generated during the learning process. 

The training and validation loss values provide important pieces of information, because they allow us to have a better insight on how the learning performance is changing over the number of epochs, and help us diagnose any problems with learning that can lead to an underfit or an overfit model. They will also inform us about the epoch at which to use the trained model weights at the inferencing stage.

Then we get to try it out:

We have seen how to train the Transformer model on a dataset of English and German sentence pairs, as well as how to plot the training and validation loss curves in order to diagnose the model’s learning performance and decide at which epoch to inference the trained model. We are now ready to inference the trained Transformer model for the purpose of translating an input sentence.

In this tutorial, you will discover how to inference the trained Transformer model for neural machine translation. 

Click through for the results and to see exactly why there’s so much computational effort dumped into high-end trained models.

Stefania Cristina continues a series on building a language transformer:

We have put together the complete Transformer model, and now we are ready to train it for neural machine translation. We shall be making use of a training dataset for this purpose, which contains short English and German sentence pairs. We will also be revisiting the role of masking in computing the accuracy and loss metrics during the training process. 

In this tutorial, you will discover how to train the Transformer model for neural machine translation. 

Read on for the process, including a lot of code.

Luke Menzies takes us through the gensim library:

An interesting branch of machine learning is Natural Language Processing (NLP). As the name suggests, it involves training machines to detect patterns in language using algorithms. It is quite often the case that NLP is referred to as text analytics. It is actually more impressive than that. It examines vectorised patterns which not only looks at the positioning of elements but what it means in context to neighbouring elements within the vector. In a nutshell, this technique can be extended beyond text to patterns of linguistics in general and even contextual patterns. Nevertheless, its primary use in the machine learning world is to analyse text.

This article will focus on an interesting application of NLP which involves the clustering of text. Clustering is a popular unsupervised machine learning technique used for segmentation or grouping of data. It is a very powerful tool that is used across a variety of industries. However, it is rare you hear of applying clustering to text. This can be achieved using NLP functions, combined with clustering algorithms that can handle non-Euclidian distances.

Read on for an overview of the process and an example of combining DBSCAN with word2vec to cluster phrases.