Introduction to Transformer for NLP with Python

Explore natural language processing and how algorithms help computers understand text. Learn tokenization, the process of splitting text into tokens, and how to install and use an nlp library.

Explore stemming and lemmatization to reduce word variants to base forms, compare Porter, Lancaster, and Snowball stemmers with lemmatization to preserve context in NLP.

Learn how to implement chunking in Python for NLP by splitting text into fixed-size word chunks from the brown corpus, iterating through chunks, and displaying the results.

Explore bag-of-words representation in NLP by converting text into a document-term matrix and a word-frequency vocabulary to build a text vector for machine learning.

Explore building a gender identification project that uses the last letter feature and a Naive Bayes classifier to predict gender from names, with training/testing, accuracy results, and parameter tuning.

Build a sentiment analyzer in python using a naive bayes classifier to label movie reviews as positive or negative, with an 80/20 train-test split.

Explore topic modeling in NLP by loading data into Colab, applying unsupervised topic modeling to uncover hidden themes, and interpreting top contributing words per topic.

Introduce the transformer, a 2017 Google NLP architecture, and show how to install the transformer library, download pre-trained models, tokenize, and generate text with beam search, sampling, and temperature control.

implement sampling in transformer-based nlp by adjusting temperature, tokenizing inputs, and applying top-k and top-p sampling to generate diverse text.

Explore transformer-based NLP workflows in Python, encoding inputs, setting sampling parameters like temperature and return sequences, and training TensorFlow models to generate and evaluate text while considering future communication impacts.

Install and load the Hugging Face transformers library, access pre-trained models for NLP, and learn tokenization, attention masks, token IDs, and embeddings.

Learn how Hugging Face transformers produce contextual token embeddings from the encoder, using attention and hidden states to represent each token within a sentence.

Learn how to extract token representations from the final encoder layer in Hugging Face transformers, inspect embeddings and hidden states, and interpret their shapes for NLP models.

Implement the IMDB project with transformers, loading the dataset, tokenizing text, and preparing training and test sets for sequence classification using a pre-trained model.

Continue IMDB project by defining training arguments, configuring the trainer with the model and data, and running training with evaluation for really good results; preview the next question answering project.

Build a transformer-based question answering project in Python, covering tokenization, model loading, and running a QA workflow with a sample immune system question.

Explore transformer-based text generation with GPT-2 in Python, covering tokenization, pre-trained models, eos tokens, and beam search, while setting up GPU runtime and reproducibility in TensorFlow.

Continue the transformer nlp project by applying beam search with max length and n-gram constraints to improve generation quality. Explore sampling settings such as temperature and top-k.

See how the model selects from the top tokens among over 50,000 options to continue text. Larger sampling values yield more surprising outputs, while smaller values stay more predictable.

Explore token classification, named entity recognition, and part-of-speech tagging, and learn how models assign each token to categories such as person, location, and organization.

Learn token classification by loading the dataset, tokenizing text with a transformer tokenizer, and preparing training inputs with tokens and labels for model training.

Learn how to implement token classification by aligning tokens with labels, preparing datasets, and loading a pretrained transformer model, then set up training arguments and run training.

implement token classification pipeline with a data collector to optimize memory, load seqeval metrics for sequence evaluation, and compute precision, recall, and accuracy during training and evaluation.

Finish training and save the model, set up token classification with a pre-trained tokenizer, align label IDs, run the model, and achieve an entity score of 97.

Implement a question‑and‑answer workflow using a text organizer, load and tokenize the dataset, and prepare multimodal inputs for a transformer‑based visual question answering model.

Learn how to implement a transformer-based q&a project in python, feeding the model a question and context separated by a special token, and handle long contexts with truncation.

Implement a q&a project using tokenization, context, and token classification with transformers in python; prepare data, map features, and train or retrain a question answering model.

Guide students to implement a training argument for a Q&A token classification project, configure evaluation strategy and learning rate, and set up a trainer with tokenized data.

Train a token classification q&a model across three epochs and monitor training and validation loss. Then set up the model and test it with a sample question and context.

Celebrate completing the course and commit to ongoing practice in deep learning; set personal goals, and download the Cargo and UCI Respiratory datasets to build and share a model.