Python Deep Learning Tips

Learn how to fine-tune the transformer models  Roberta, Electra, and BERT, T5, and Alberta in Python in this free beginner intro to fine tuning with hugging faces with a simple NLP classification problem. In this free Python educational lesson, we will use transformers' Auto Model class to easily import each different model with similar Python code.  This pairs nicely with the Auto Tokenizer which helps ensure our texts are tokenized in the appropriate format for each model without having to import a separate tokenizer or model function.  

Using the Auto Model and Auto Tokenizer makes setting up experiments in with transforms so much faster and easier.

Here we Fine Tune these various sequence classification model on a New Articles classification problem with 4 classes.  To show the effect of fine tuning with will look at the model's accuracy and confusion matrix first without fine tuning and then again after we h have completed fine tuning.  In these free Python lesson we will be training these models for only 3 epochs and it's amazing the effect of such a short training time on the final accuracy. 

In the lesson we will introduce NLP Pipelines in Hugging Faces.  We will show how to use a transformers pipeline for sentiment classification to start.  And then show how the pipeline can be used to classify different texts in any number of different categories with incredible ease with zero-shot classification.  The value of pipelines is that they combine the tokenization and model and then the decoding into one very very easy-to-use function.  

With pipelines, we'll have the flexibility to use the many different models as well.  From GPT2, RoBerta, BERT, and one our favorite Electra.  Pipelines will handle the individual tokenization specific to each model and then the return to use the sentiment, the classification, and even the generated text. 

Learn how simple and easy it is to use the a Hugging Faces Pipeline in the free Python machine-learning class. 

In this Hugging Faces Tips Video we will use Python and learn how to manipulate and explore a Transformers dataset.  First we need to know how to load a dataset from Hugging Faces Hub and we will show you how to do this and then load the data with the datasets library. 

To Tokenizer or TokenizerFast? What's the difference between these two tokenizers in Hugging Faces transformer library and when would it make sense to use one versus the other?    We'll use a 650,000 row, Yelp Review, dataset to compare the speed of each tokenizer and see if Fast really means faster.  In this lesson, we will use ElectraTokenizer and ElectraFastTokenizer although the would work with any of the models in Hugging Faces that have the fast version available.  We'll also show you how to decode the input ids back to words after tokenization.

Transformers have proven themselves as crucial in many Deep Learning tasks such as text classification in an NLP problem.  Here we will use a dataset from Hugging Faces dataset library and train For Sequences classification versions of the Roberta, Electra, XLNet, Deberta, RoFormer, and BERT transformer models.  We will compare how each of these models did by comparing the accuracy of train and test predictions.   We'll also be comparing training times.  We look at retraining only the classification output layer and then training the entire model including the transformer to see the final results.