- 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 - Info Command
As name implies, this command will print the information about −
spaCy installation
models
local setups
It also generates Markdown-formatted markup to copy-paste into GitHub issues.
The Info command is as follows −
python -m spacy info [model] [--markdown] [--silent]
Arguments
The table below explains its arguments −
ARGUMENT | TYPE | DESCRIPTION |
---|---|---|
Model | positional | Here we need to provide the model name whether, it is package, or path (optional). |
--markdown, -md | Flag | It will print the information as Markdown. |
--silent, -s | Flag | It will not print anything but just return the values. This feature is new and was introduced in version2.0.12. |
--help, -h | Flag | This argument will show help message and other available arguments. |
Example
The below command will give the info about en_core_web_sm installed model.
C:\Users\Leekha>python -m spacy info en_core_web_sm
Output
This produces the following output −
===================== Info about model 'en_core_web_sm' ===================== lang en name core_web_sm license MIT author Explosion url https://explosion.ai email contact@explosion.ai description English multi-task CNN trained on OntoNotes. Assigns context-specific token vectors, POS tags, dependency parse and named entities. sources [{'name': 'OntoNotes 5', 'url': 'https://catalog.ldc.upenn.edu/LDC2013T19', 'license': 'commercial (licensed by Explosion)'}] pipeline ['tagger', 'parser', 'ner'] version 2.2.0 spacy_version >=2.2.0 parent_package spacy labels {'tagger': ['$', "''", ',', '-LRB-', '-RRB-', '.', ':', 'ADD', 'AFX', 'CC', 'CD', 'DT', 'EX', 'FW', 'HYPH', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NFP', 'NN', 'NNP', 'NNPS', 'NNS', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB', 'XX', '_SP', '``'], 'parser': ['ROOT', 'acl', 'acomp', 'advcl', 'advmod', 'agent', 'amod', 'appos', 'attr', 'aux', 'auxpass', 'case', 'cc', 'ccomp', 'compound', 'conj', 'csubj', 'csubjpass', 'dative', 'dep', 'det', 'dobj', 'expl', 'intj', 'mark', 'meta', 'neg', 'nmod', 'npadvmod', 'nsubj', 'nsubjpass', 'nummod', 'oprd', 'parataxis', 'pcomp', 'pobj', 'poss', 'preconj', 'predet', 'prep', 'prt', 'punct', 'quantmod', 'relcl', 'xcomp'], 'ner': ['CARDINAL', 'DATE', 'EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'MONEY', 'NORP', 'ORDINAL', 'ORG', 'PERCENT', 'PERSON', 'PRODUCT', 'QUANTITY', 'TIME', 'WORK_OF_ART']} source C:\Users\Leekha\Anaconda3\lib\site-packages\en_core_web_sm
spacy_command_line_helpers.htm
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