lambeq
0.3.1 [git latest]
Getting started
Installation
Troubleshooting
Pipeline
Syntactic parsing
String diagrams
DisCoPy
lambeq use cases
Contributing to lambeq
NLP-101
Introduction
Working with text data
Text classification
Machine learning best practices
References for further study
Tutorials
Step 1. Sentence input
Step 2. Diagram rewriting
Step 3. Parameterisation
Step 4: Training
Choosing a model
Advanced: Manual training
Advanced: DisCoPy usage
Advanced: Extending lambeq
Examples
Tokenisation
Parser
Reader
Tree reader
Rewrite
Circuit
Tensor
Classical pipeline
Quantum pipeline using the Quantum Trainer
Quantum pipeline using JAX backend
Training hybrid models using the Pennylane backend
Toolkit
lambeq package
Subpackages
Class diagrams
Command-line interface
Reference
Glossary
Bibliography
Index
Release notes
External links
Resources
Web demo
DisCoPy
lambeq
Examples
Edit on GitHub
Examples
Tokenisation
Word tokenisation
Splitting a document into sentences
Parser
Reader
Tree reader
Rewrite
Auxiliary rule
Connector rule
Determiner rule
Adverb rules
Prepositional phrase rule
Relative Pronoun rules
Coordination
Remove cups
Curry functor
Circuit
Tensor
Classical pipeline
Input data
Create diagrams
Create circuits
Parameterise
Define Evaluation Metric
Initialize Trainer
Train
Show results
Quantum pipeline using the Quantum Trainer
Read in the data and create diagrams
Create diagrams
Remove the cups
Create circuits
Parameterise
Define evaluation metric
Initialize trainer
Train
Show results
Quantum pipeline using JAX backend
Read in the data and create diagrams
Create diagrams
Remove the cups
Create circuits
Parameterise
Define evaluation metric
Initialize trainer
Train
Show results
Training hybrid models using the Pennylane backend
Read in the data and create diagrams
Remove cups
Create DisCoPy circuits
Create (pure quantum) model and initialise parameters
Prepare train dataset
Training
Using
PytorchTrainer
Determine test accuracy
Using standard PyTorch
Determine the test accuracy
Creating a hybrid model
Make paired dataset
Initialise the model
Train the model and log accuracies