💎 ruby-spacy

Overview

ruby-spacy is a wrapper module for using spaCy from the Ruby programming language via PyCall. This module aims to make it easy and natural for Ruby programmers to use spaCy. This module covers the areas of spaCy functionality for using many varieties of its language models, not for building ones.

	Functionality
✅	Tokenization, lemmatization, sentence segmentation
✅	Part-of-speech tagging and dependency parsing
✅	Named entity recognition
✅	Syntactic dependency visualization
✅	Access to pre-trained word vectors
✅	OpenAI Chat/Completion/Embeddings API integration

Current Version: 0.2.2

• spaCy 3.7.0 supported
• OpenAI API integration

Installation of Prerequisites

IMPORTANT: Make sure that the enable-shared option is enabled in your Python installation. You can use pyenv to install any version of Python you like. Install Python 3.10.6, for instance, using pyenv with enable-shared as follows:

$ env CONFIGURE_OPTS="--enable-shared" pyenv install 3.10.6

Remember to make it accessible from your working directory. It is recommended that you set global to the version of python you just installed.

$ pyenv global 3.10.6 

Then, install spaCy. If you use pip, the following command will do:

$ pip install spacy

Install trained language models. For a starter, en_core_web_sm will be the most useful to conduct basic text processing in English. However, if you want to use advanced features of spaCy, such as named entity recognition or document similarity calculation, you should also install a larger model like en_core_web_lg.

$ python -m spacy download en_core_web_sm
$ python -m spacy download en_core_web_lg

See Spacy: Models & Languages for other models in various languages. To install models for the Japanese language, for instance, you can do it as follows:

$ python -m spacy download ja_core_news_sm
$ python -m spacy download ja_core_news_lg

Installation of ruby-spacy

Add this line to your application’s Gemfile:

gem 'ruby-spacy'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install ruby-spacy

Usage

See Examples below.

Examples

Many of the following examples are Python-to-Ruby translations of code snippets in spaCy 101. For more examples, look inside the examples directory.

Tokenization

→ spaCy: Tokenization

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")

doc = nlp.read("Apple is looking at buying U.K. startup for $1 billion")

row = []

doc.each do |token|
  row << token.text
end

headings = [1,2,3,4,5,6,7,8,9,10]
table = Terminal::Table.new rows: [row], headings: headings

puts table

Output:

1	2	3	4	5	6	7	8	9	10	11
Apple	is	looking	at	buying	U.K.	startup	for	$	1	billion

Part-of-speech and Dependency

→ spaCy: Part-of-speech tags and dependencies

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Apple is looking at buying U.K. startup for $1 billion")

headings = ["text", "lemma", "pos", "tag", "dep"]
rows = []

doc.each do |token|
  rows << [token.text, token.lemma, token.pos, token.tag, token.dep]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text	lemma	pos	tag	dep
Apple	Apple	PROPN	NNP	nsubj
is	be	AUX	VBZ	aux
looking	look	VERB	VBG	ROOT
at	at	ADP	IN	prep
buying	buy	VERB	VBG	pcomp
U.K.	U.K.	PROPN	NNP	dobj
startup	startup	NOUN	NN	advcl
for	for	ADP	IN	prep
$	$	SYM	$	quantmod
1	1	NUM	CD	compound
billion	billion	NUM	CD	pobj

Part-of-speech and Dependency (Japanese)

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("ja_core_news_lg")
doc = nlp.read("任天堂は1983年にファミコンを14,800円で発売した。")

headings = ["text", "lemma", "pos", "tag", "dep"]
rows = []

doc.each do |token|
  rows << [token.text, token.lemma, token.pos, token.tag, token.dep]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text	lemma	pos	tag	dep
任天堂	任天堂	PROPN	名詞-固有名詞-一般	nsubj
は	は	ADP	助詞-係助詞	case
1983	1983	NUM	名詞-数詞	nummod
年	年	NOUN	名詞-普通名詞-助数詞可能	obl
に	に	ADP	助詞-格助詞	case
ファミコン	ファミコン	NOUN	名詞-普通名詞-一般	obj
を	を	ADP	助詞-格助詞	case
14,800	14,800	NUM	名詞-数詞	fixed
円	円	NOUN	名詞-普通名詞-助数詞可能	obl
で	で	ADP	助詞-格助詞	case
発売	発売	VERB	名詞-普通名詞-サ変可能	ROOT
し	する	AUX	動詞-非自立可能	aux
た	た	AUX	助動詞	aux
。	。	PUNCT	補助記号-句点	punct

Morphology

→ POS and morphology tags

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Apple is looking at buying U.K. startup for $1 billion")

headings = ["text", "shape", "is_alpha", "is_stop", "morphology"]
rows = []

doc.each do |token|
  morph = token.morphology.map do |k, v|
    "#{k} = #{v}"
  end.join("\n")
  rows << [token.text, token.shape, token.is_alpha, token.is_stop, morph]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text	shape	is_alpha	is_stop	morphology
Apple	Xxxxx	true	false	NounType = Prop Number = Sing
is	xx	true	true	Mood = Ind Number = Sing Person = 3 Tense = Pres VerbForm = Fin
looking	xxxx	true	false	Aspect = Prog Tense = Pres VerbForm = Part
at	xx	true	true
buying	xxxx	true	false	Aspect = Prog Tense = Pres VerbForm = Part
U.K.	X.X.	false	false	NounType = Prop Number = Sing
startup	xxxx	true	false	Number = Sing
for	xxx	true	true
$	$	false	false
1	d	false	false	NumType = Card
billion	xxxx	true	false	NumType = Card

Visualizing Dependency

→ spaCy: Visualizers

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("en_core_web_sm")

sentence = "Autonomous cars shift insurance liability toward manufacturers"
doc = nlp.read(sentence)

dep_svg = doc.displacy(style: "dep", compact: false)

File.open(File.join("test_dep.svg"), "w") do |file|
  file.write(dep_svg)
end

Output:

Visualizing Dependency (Compact)

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("en_core_web_sm")

sentence = "Autonomous cars shift insurance liability toward manufacturers"
doc = nlp.read(sentence)

dep_svg = doc.displacy(style: "dep", compact: true)

File.open(File.join("test_dep_compact.svg"), "w") do |file|
  file.write(dep_svg)
end

Output:

Named Entity Recognition

→ spaCy: Named entities

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")
doc =nlp.read("Apple is looking at buying U.K. startup for $1 billion")

rows = []

doc.ents.each do |ent|
  rows << [ent.text, ent.start_char, ent.end_char, ent.label]
end

headings = ["text", "start_char", "end_char", "label"]
table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text	start_char	end_char	label
Apple	0	5	ORG
U.K.	27	31	GPE
$1 billion	44	54	MONEY

Named Entity Recognition (Japanese)

Ruby code:

require( "ruby-spacy")
require "terminal-table"

nlp = Spacy::Language.new("ja_core_news_lg")

sentence = "任天堂は1983年にファミコンを14,800円で発売した。"
doc = nlp.read(sentence)

rows = []

doc.ents.each do |ent|
  rows << [ent.text, ent.start_char, ent.end_char, ent.label]
end

headings = ["text", "start", "end", "label"]
table = Terminal::Table.new rows: rows, headings: headings
print table

Output:

text	start	end	label
任天堂	0	3	ORG
1983年	4	9	DATE
ファミコン	10	15	PRODUCT
14,800円	16	23	MONEY

Checking Availability of Word Vectors

→ spaCy: Word vectors and similarity

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_lg")
doc = nlp.read("dog cat banana afskfsd")

rows = []

doc.each do |token|
  rows << [token.text, token.has_vector, token.vector_norm, token.is_oov]
end

headings = ["text", "has_vector", "vector_norm", "is_oov"]
table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text	has_vector	vector_norm	is_oov
dog	true	7.0336733	false
cat	true	6.6808186	false
banana	true	6.700014	false
afskfsd	false	0.0	true

Similarity Calculation

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("en_core_web_lg")
doc1 = nlp.read("I like salty fries and hamburgers.")
doc2 = nlp.read("Fast food tastes very good.")

puts "Doc 1: " + doc1.text
puts "Doc 2: " + doc2.text
puts "Similarity: #{doc1.similarity(doc2)}"

Output:

Doc 1: I like salty fries and hamburgers.
Doc 2: Fast food tastes very good.
Similarity: 0.7687607012190486

Similarity Calculation (Japanese)

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("ja_core_news_lg")
ja_doc1 = nlp.read("今日は雨ばっかり降って、嫌な天気ですね。")
puts "doc1: #{ja_doc1.text}"
ja_doc2 = nlp.read("あいにくの悪天候で残念です。")
puts "doc2: #{ja_doc2.text}"
puts "Similarity: #{ja_doc1.similarity(ja_doc2)}"

Output:

doc1: 今日は雨ばっかり降って、嫌な天気ですね。
doc2: あいにくの悪天候で残念です。
Similarity: 0.8684192637149641

Word Vector Calculation

Tokyo - Japan + France = Paris ?

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_lg")

tokyo = nlp.get_lexeme("Tokyo")
japan = nlp.get_lexeme("Japan")
france = nlp.get_lexeme("France")

query = tokyo.vector - japan.vector + france.vector

headings = ["rank", "text", "score"]
rows = []

results = nlp.most_similar(query, 10)
results.each_with_index do |lexeme, i|
  index = (i + 1).to_s
  rows << [index, lexeme.text, lexeme.score]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

rank	text	score
1	FRANCE	0.8346999883651733
2	France	0.8346999883651733
3	france	0.8346999883651733
4	PARIS	0.7703999876976013
5	paris	0.7703999876976013
6	Paris	0.7703999876976013
7	TOULOUSE	0.6381999850273132
8	Toulouse	0.6381999850273132
9	toulouse	0.6381999850273132
10	marseille	0.6370999813079834

Word Vector Calculation (Japanese)

東京 - 日本 + フランス = パリ ?

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("ja_core_news_lg")

tokyo = nlp.get_lexeme("東京")
japan = nlp.get_lexeme("日本")
france = nlp.get_lexeme("フランス")

query = tokyo.vector - japan.vector + france.vector

headings = ["rank", "text", "score"]
rows = []

results = nlp.most_similar(query, 10)
results.each_with_index do |lexeme, i|
  index = (i + 1).to_s
  rows << [index, lexeme.text, lexeme.score]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

rank	text	score
1	パリ	0.7376999855041504
2	フランス	0.7221999764442444
3	東京	0.6697999835014343
4	ストラスブール	0.631600022315979
5	リヨン	0.5939000248908997
6	Paris	0.574400007724762
7	ベルギー	0.5683000087738037
8	ニース	0.5679000020027161
9	アルザス	0.5644999742507935
10	南仏	0.5547999739646912

OpenAI API Integration

⚠️ This feature is currently experimental. Details are subject to change. Please refer to OpenAI’s API reference and Ruby OpenAI for available parameters (max_tokens, temperature, etc).

Easily leverage GPT models within ruby-spacy by using an OpenAI API key. When constructing prompts for the Doc::openai_query method, you can incorporate the following token properties of the document. These properties are retrieved through function calls (made internally by GPT when necessary) and seamlessly integrated into your prompt. Note that function calls need gpt-3.5-turbo-0613 or higher. The available properties include:

• surface
• lemma
• tag
• pos (part of speech)
• dep (dependency)
• ent_type (entity type)
• morphology

GPT Prompting (Translation)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles released 12 studio albums")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-3.5-turbo-0613"
res1 = doc.openai_query(
  access_token: api_key,
  prompt: "Translate the text to Japanese."
)
puts res1

Output:

ビートルズは12枚のスタジオアルバムをリリースしました。

GPT Prompting (Elaboration)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles were an English rock band formed in Liverpool in 1960.")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-3.5-turbo-0613"
res = doc.openai_query(
  access_token: api_key,
  prompt: "Extract the topic of the document and list 10 entities (names, concepts, locations, etc.) that are relevant to the topic."
)

Output:

Topic: The Beatles

Entities:

1. The Beatles (band)
2. English (nationality)
3. Rock band
4. Liverpool (city)
5. 1960 (year)
6. John Lennon (member)
7. Paul McCartney (member)
8. George Harrison (member)
9. Ringo Starr (member)
10. Music

GPT Prompting (JSON Output Using RAG with Token Properties)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles released 12 studio albums")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-3.5-turbo-0613"
res = doc.openai_query(
  access_token: api_key,
  prompt: "List token data of each of the words used in the sentence. Add 'meaning' property and value (brief semantic definition) to each token data. Output as a JSON object."
)

Output:

{
  "tokens": [
    {
      "surface": "The",
      "lemma": "the",
      "pos": "DET",
      "tag": "DT",
      "dep": "det",
      "ent_type": "",
      "morphology": "{'Definite': 'Def', 'PronType': 'Art'}",
      "meaning": "Used to refer to one or more people or things already mentioned or assumed to be common knowledge"
    },
    {
      "surface": "Beatles",
      "lemma": "beatle",
      "pos": "NOUN",
      "tag": "NNS",
      "dep": "nsubj",
      "ent_type": "GPE",
      "morphology": "{'Number': 'Plur'}",
      "meaning": "A British rock band formed in Liverpool in 1960"
    },
    {
      "surface": "released",
      "lemma": "release",
      "pos": "VERB",
      "tag": "VBD",
      "dep": "ROOT",
      "ent_type": "",
      "morphology": "{'Tense': 'Past', 'VerbForm': 'Fin'}",
      "meaning": "To make something available or known to the public"
    },
    {
      "surface": "12",
      "lemma": "12",
      "pos": "NUM",
      "tag": "CD",
      "dep": "nummod",
      "ent_type": "CARDINAL",
      "morphology": "{'NumType': 'Card'}",
      "meaning": "A number representing a quantity"
    },
    {
      "surface": "studio",
      "lemma": "studio",
      "pos": "NOUN",
      "tag": "NN",
      "dep": "compound",
      "ent_type": "",
      "morphology": "{'Number': 'Sing'}",
      "meaning": "A place where creative work is done"
    },
    {
      "surface": "albums",
      "lemma": "album",
      "pos": "NOUN",
      "tag": "NNS",
      "dep": "dobj",
      "ent_type": "",
      "morphology": "{'Number': 'Plur'}",
      "meaning": "A collection of musical or spoken recordings"
    }
  ]
}

GPT Prompting (Generate a Syntaxt Tree using Token Properties)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles released 12 studio albums")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
res = doc.openai_query(
  access_token: api_key,
  model: "gpt-4",
  prompt: "Generate a tree diagram from the text using given token data. Use the following bracketing style: [S [NP [Det the] [N cat]] [VP [V sat] [PP [P on] [NP the mat]]]"
)
puts res

Output:

[S
  [NP
    [Det The]
    [N Beatles]
  ]
  [VP
    [V released]
    [NP
      [Num 12]
      [N
        [N studio]
        [N albums]
      ]
    ]
  ]
]

GPT Text Completion

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Vladimir Nabokov was a")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-3.5-turbo-0613"
res = doc.openai_completion(access_token: api_key)
puts res

Output:

Russian-American novelist and lepidopterist. He was born in 1899 in St. Petersburg, Russia, and later emigrated to the United States in 1940. Nabokov is best known for his novel “Lolita,” which was published in 1955 and caused much controversy due to its controversial subject matter. Throughout his career, Nabokov wrote many other notable works, including “Pale Fire” and “Ada or Ardor: A Family Chronicle.” In addition to his writing, Nabokov was also a passionate butterfly collector and taxonomist, publishing several scientific papers on the subject. He passed away in 1977, leaving behind a rich literary legacy.

Text Embeddings

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Vladimir Nabokov was a Russian-American novelist, poet, translator and entomologist.")

# default model: text-embedding-ada-002
res = doc.openai_embeddings(access_token: api_key)

puts res

Output:

-0.00208362
-0.01645165
 0.0110955965
 0.012802119
 0.0012175755
 ...

Author

Yoichiro Hasebe [[email protected]]

Acknowlegments

I would like to thank the following open source projects and their creators for making this project possible:

• explosion/spaCy
• mrkn/pycall.rb

License

This library is available as open source under the terms of the MIT License.