IIRF Online > Development > Data Science > Natural Language Processing > Natural Language Processing: NLP With Transformers in Python
Natural Language Processing: NLP With Transformers in Python by Udemy
Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more
Course Highlights
- Industry standard NLP using transformer models
- Build full-stack question-answering transformer models
- Perform sentiment analysis with transformers models in PyTorch and TensorFlow
- Advanced search technologies like Elasticsearch and Facebook AI Similarity Search (FAISS)
- Create fine-tuned transformers models for specialized use-cases
- Measure performance of language models using advanced metrics like ROUGE
- Vector building techniques like BM25 or dense passage retrievers (DPR)
- An overview of recent developments in NLP
- Understand attention and other key components of transformers
- Learn about key transformers models such as BERT
- Preprocess text data for NLP
- Named entity recognition (NER) using spaCy and transformers
- Fine-tune language classification models
Skills you will learn!
Curriculum
8 Topics
Introduction
Course Overview
Hello! and Further Resources
Environment Setup
Alternative Local Setup
Alternative Colab Setup
CUDA Setup
Apple Silicon Setup
10 Topics
The Three Eras of AI
Pros and Cons of Neural AI
Word Vectors
Recurrent Neural Networks
Long Short-Term Memory
Encoder-Decoder Attention
Self-Attention
Multi-head Attention
Positional Encoding
Transformer Heads
9 Topics
Stopwords
Tokens Introduction
Model-Specific Special Tokens
Stemming
Lemmatization
Unicode Normalization - Canonical and Compatibility Equivalence
Unicode Normalization - Composition and Decomposition
Unicode Normalization - NFD and NFC
Unicode Normalization - NFKD and NFKC
6 Topics
Attention Introduction
Alignment With Dot-Product
Dot-Product Attention
Self Attention
Bidirectional Attention
Multi-head and Scaled Dot-Product Attention
5 Topics
Introduction to Sentiment Analysis
Prebuilt Flair Models
Introduction to Sentiment Models With Transformers
Tokenization And Special Tokens For BERT
Making Predictions
7 Topics
Project Overview
Getting the Data (Kaggle API)
Preprocessing
Building a Dataset
Dataset Shuffle Batch Split and Save
Build and Save
Loading and Prediction
2 Topics
Classification of Long Text Using Windows
Window Method in PyTorch
10 Topics
Introduction to spaCy
Extracting Entities
NER Walkthrough
Authenticating With The Reddit API
Pulling Data With The Reddit API
Extracting ORGs From Reddit Data
Getting Entity Frequency
Entity Blacklist
NER With Sentiment
NER With roBERTa
7 Topics
Open Domain and Reading Comprehension
Retrievers Readers and Generators
Intro to SQuAD 2.0
Processing SQuAD Training Data
(Optional) Processing SQuAD Training Data with Match-Case
Processing SQuAD Dev Data
Our First Q&A Model
6 Topics
Q&A Performance With Exact Match (EM)
Introducing the ROUGE Metric
ROUGE in Python
Applying ROUGE to Q&A
Recall Precision and F1
Longest Common Subsequence (LCS)
14 Topics
Intro to Retriever-Reader and Haystack
What is Elasticsearch?
Elasticsearch Setup (Windows)
Elasticsearch Setup (Linux)
Elasticsearch in Haystack
Sparse Retrievers
Cleaning the Index
Implementing a BM25 Retriever
What is FAISS?
Further Materials for Faiss
FAISS in Haystack
What is DPR?
The DPR Architecture
Retriever-Reader Stack
3 Topics
ODQA Stack Structure
Creating the Database
Building the Haystack Pipeline
6 Topics
Introduction to Similarity
Extracting The Last Hidden State Tensor
Sentence Vectors With Mean Pooling
Using Cosine Similarity
Similarity With Sentence-Transformers
Further Learning
15 Topics
Visual Guide to BERT Pretraining
Introduction to BERT For Pretraining Code
BERT Pretraining - Masked-Language Modeling (MLM)
BERT Pretraining - Next Sentence Prediction (NSP)
The Logic of MLM
Pre-training with MLM - Data Preparation
Pre-training with MLM - Training
Pre-training with MLM - Training with Trainer
The Logic of NSP
Pre-training with NSP - Data Preparation
Pre-training with NSP - DataLoader
Setup the NSP Pre-training Training Loop
The Logic of MLM and NSP
Pre-training with MLM and NSP - Data Preparation
Setup DataLoader and Model Pre-training For MLM and NSP
Natural Language Processing: NLP With Transformers in Python
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