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Spacy-cpp is a C++ wrapper library for the NLP library spaCy. This project is not affiliated with spaCy, it is however distributed under the same type of license (MIT).

The goal of spacy-cpp is to expose the functionality of spaCy to C++ applications, and to provide an API that is similar to that of spaCy, enabling rapid development in Python and simple porting to C++.

Spacy-cpp is under development and does not yet support all API's of spaCy, refer to the API Documentation section below.

Simple POS tagging example using spacy-cpp:

Spacy::Spacy spacy;
auto nlp = spacy.load("en_core_web_sm");
auto doc = nlp.parse("This is a sentence.");
for (auto& token : doc.tokens())
    std::cout << token.text() << " [" << token.pos_() << "]\n";

For reference - doing the same using the spaCy API in Python:

import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp(u"This is a sentence.")
for token in doc:
    print(token.text + " [" + token.pos_ + "]")

Spacy-cpp is implemented using C++11 with the intention of being portable. Current version has been tested on:

• Ubuntu 22.04 LTS

Spacy-cpp requires python development library, pip, spaCy and typically a spaCy model.

Install build dependencies:

sudo apt install cmake python3-pip libpython3-dev

Install spaCy and an English model:

pip3 install -U spacy
python3 -m spacy download en_core_web_sm

Spacy-cpp can be used either as a shared library or as a header-only library.

Build and install spacy-cpp:

mkdir -p build && cd build && cmake .. && make && sudo make install

Link library:

-lspacy

Include header (convenience header including all modules):

#include <spacy/spacy>

Copy the src/spacy directory to the source directory of your project. Then define SPACY_HEADER_ONLY and include headers needed (spacy/spacy includes all headers):

#define SPACY_HEADER_ONLY
#include <spacy/spacy>

Spacy-cpp is under development and does not support the complete spaCy API yet.

• Attrs
• Doc
• Nlp
• Spacy
• Span
• StringStore
• Token
• Vocab

Attrs supports all attribute constants.

Doc supports the following methods / attributes:

• count_by()
• ents()
• has_vector()
• is_parsed()
• is_tagged()
• noun_chunks()
• sentiment()
• sents()
• similarity()
• text()
• text_with_ws()
• tokens()
• vector_norm()

Nlp supports the following methods / attributes:

• parse()
• vocab()

Spacy supports the following methods / attributes:

• load()
• attrs()

Span supports the following methods / attributes:

• doc()
• label()
• label_()
• lemma_()
• orth_()
• root()
• sentiment()
• text()
• text_with_ws()
• tokens()
• vector_norm()

StringStore supports the following methods / attributes:

• add()

Token supports the following methods / attributes:

• check_flag()
• children()
• cluster()
• dep()
• dep_()
• ent_iob_()
• has_vector()
• head()
• i()
• idx()
• is_alpha()
• is_ascii()
• is_bracket()
• is_digit()
• is_left_punct()
• is_lower()
• is_oov()
• is_punct()
• is_quote()
• is_right_punct()
• is_space()
• is_stop()
• is_title()
• is_upper()
• lang()
• lang_()
• lemma()
• lemma_()
• like_email()
• like_num()
• like_url()
• lower()
• lower_()
• nbor()
• norm()
• norm_()
• orth()
• orth_()
• pos()
• pos_()
• prob()
• rank()
• sentiment()
• shape()
• shape_()
• tag()
• tag_()
• text()
• text_with_ws()
• whitespace_()

Vocab supports the following methods / attributes:

• strings()

1. In spacy-cpp Nlp cannot be called as a method in order to perform parsing. Instead one need to use Nlp::parse().
2. In spacy-cpp Doc is not an iterable, instead one need to use Doc::token() to get a std::vector of the tokens in the Doc. Likewise for Span.
3. In spacy-cpp non-ASCII strings must be UTF-8 encoded in order to be correctly processed.

Spacy-cpp uses cmake for its tests. Commands to build and execute the test suite:

mkdir -p build && cd build && cmake -DSPACYCPP_BUILD_TESTS=ON .. && make && ctest --output-on-failure ; cd -

Spacy-cpp is distributed under the MIT license. See LICENSE file.

Bugs, PRs, etc are welcome on the GitHub project page https://github.com/d99kris/spacy-cpp

c++, c++11, natural language processing, nlp, spacy.