- spaCy Tutorial
- spaCy - Home
- spaCy - Introduction
- spaCy - Getting Started
- spaCy - Models and Languages
- spaCy - Architecture
- spaCy - Command Line Helpers
- spaCy - Top-level Functions
- spaCy - Visualization Function
- spaCy - Utility Functions
- spaCy - Compatibility Functions
- spaCy - Containers
- Doc Class ContextManager and Property
- spaCy - Container Token Class
- spaCy - Token Properties
- spaCy - Container Span Class
- spaCy - Span Class Properties
- spaCy - Container Lexeme Class
- Training Neural Network Model
- Updating Neural Network Model
- spaCy Useful Resources
- spaCy - Quick Guide
- spaCy - Useful Resources
- spaCy - Discussion
spaCy - Command Line Helpers
This chapter gives information about the command line helpers used in spaCy.
Why Command Line Interface?
spaCy v1.7.0 and above comes with new command line helpers. It is used to download as well as link the models. You can also use it to show the useful debugging information. In short, command line helpers are used to download, train, package models, and also to debug spaCy.
Checking Available Commands
You can check the available commands by using spacy - -help command.
The example to check the available commands in spaCy is given below −
Example
C:\Users\Leekha>python -m spacy --help
Output
The output shows the available commands.
Available commands download, link, info, train, pretrain, debug-data, evaluate, convert, package, init-model, profile, validate
Available Commands
The commands available in spaCy are given below along with their respective descriptions.
Sr.No. | Command & Description |
---|---|
1 | Download To download models for spaCy. |
2 | Link To create shortcut links for models. |
3 | Info To print the information. |
4 | Validate To check compatibility of the installed models. |
5 | Convert To convert the files into spaCy's JSON format. |
6 | Pretrain To pre-train the “token to vector (tok2vec)” layer of pipeline components. |
7 | Init-model To create a new model directory from raw data. |
8 | Evaluate To evaluate a model's accuracy and speed. |
9 | Package To generate a model python package from an existing model data directory. |
10 | Debug-data To analyse, debug, and validate our training and development data. |
11 | Train To train a model. |
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