Tutorials on Working with Hugging Face Models and Datasets
This is a series of short tutorials for working with Hugging Face models and datasets for several NLP tasks. The tutorials are structured as follows:
Chapter 1: Getting Familiar with the Hugging Face Ecosystem
Lesson 1.1: Using a pre-trained transformer model and tokenizer in Hugging Face to classify text.
Lesson 1.2: Loading and exploring a dataset from Hugging Face
Lesson 1.3: Creating an access token and logging into Hugging Face hub from a notebook
Chapter 2: Named Entity Recognition (NER) Using Models in Hugging Face
Lesson 2.1: Named Entity Recognition (NER) by directly using the bert-base-NER model in Hugging Face
Lesson 2.3: Fine-tuning the bert-base-NER model in Hugging Face for Named Entity Recognition (NER)
Chapter 3: Text Summarization Using Models in Hugging Face
Lesson 3.1: Utilizing ChatGPT to navigate the usage of Hugging Face for text summarization
Lesson 3.2: Fine-tuning the pre-trained t5-small model in Hugging Face for text summarization
Chapter 4: Question-Answering Using Models in Hugging Face
Lesson 4.1: Question answering using a pre-trained model in Hugging Face
Lesson 4.2: Fine-tuning the pre-trained BERT model on SQuAD in Hugging Face for question answering
Please let me know what else I should add to the series of tutorials. In addition, I am happy to create tutorials on other data science and programming topics. Thank you!
