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//- 💫 DOCS > USAGE > ADDING LANGUAGES
include ../../_includes/_mixins
p
| Adding full support for a language touches many different parts of the
| spaCy library. This guide explains how to fit everything together, and
| points you to the specific workflows for each component. Obviously,
| there are lots of ways you can organise your code when you implement
| your own #[+api("language") #[code Language]] class. This guide will
| focus on how it's done within spaCy. For full language support, we'll
| need to:
+list("numbers")
+item Create a #[strong #[code Language] subclass].
+item
| Define custom #[strong language data], like a
| #[a(href="#stop-words") stop list] and
| #[a(href="#tokenizer-exceptions") tokenizer exceptions].
+item
| #[strong Build the vocabulary] including
| #[a(href="#word-frequencies") word frequencies],
| #[a(href="#brown-clusters") Brown clusters] and
| #[a(href="#word-vectors") word vectors].
+item
| #[strong Set up] a #[a(href="#model-directory") model direcory] and
| #[strong train] the #[a(href="#train-tagger-parser") tagger and parser].
p
| For some languages, you may also want to develop a solution for
| lemmatization and morphological analysis.
+h(2, "language-subclass") Creating a #[code Language] subclass
p
| Language-specific code and resources should be organised into a
| subpackage of spaCy, named according to the language's
| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code].
| For instance, code and resources specific to Spanish are placed into a
| directory #[code spacy/lang/es], which can be imported as
| #[code spacy.lang.es].
p
| To get started, you can use our
| #[+src(gh("spacy-dev-resources", "templates/new_language")) templates]
| for the most important files. Here's what the class template looks like:
+code("__init__.py (excerpt)").
# import language-specific data
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .lex_attrs import LEX_ATTRS
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ...language import Language
from ...attrs import LANG
from ...util import update_exc
class Xxxxx(Language):
lang = 'xx' # language ISO code
# override defaults
class Defaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: 'xx' # language ISO code
# optional: replace flags with custom functions, e.g. like_num()
lex_attr_getters.update(LEX_ATTRS)
# merge base exceptions and custom tokenizer exceptions
tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
stop_words = set(STOP_WORDS)
# set default export – this allows the language class to be lazy-loaded
__all__ = ['Xxxxx']
+aside("Why lazy-loading?")
| Some languages contain large volumes of custom data, like lemmatizer
| loopup tables, or complex regular expression that are expensive to
| compute. As of spaCy v2.0, #[code Language] classes are not imported on
| initialisation and are only loaded when you import them directly, or load
| a model that requires a language to be loaded. To lazy-load languages in
| your application, you can use the #[code util.load_lang_class()] helper
| function with the two-letter language code as its argument.
+h(2, "language-data") Adding language data
p
| Every language is full of exceptions and special cases, especially
| amongst the most common words. Some of these exceptions are shared
| between multiple languages, while others are entirely idiosyncratic.
| spaCy makes it easy to deal with these exceptions on a case-by-case
| basis, by defining simple rules and exceptions. The exceptions data is
| defined in Python the
| #[+src(gh("spacy-dev-resources", "templates/new_language")) language data],
| so that Python functions can be used to help you generalise and combine
| the data as you require.
p
| Here's an overview of the individual components that can be included
| in the language data. For more details on them, see the sections below.
+image
include ../../assets/img/docs/language_data.svg
+table(["File name", "Variables", "Description"])
+row
+cell #[+src(gh()) stop_words.py]
+cell #[code STOP_WORDS] (set)
+cell
| List of most common words. Matching tokens will return #[code True]
| for #[code is_stop].
+row
+cell #[+src(gh()) tokenizer_exceptions.py]
+cell #[code TOKENIZER_EXCEPTIONS] (dict), #[code TOKEN_MATCH] (regex)
+cell
| Special-case rules for the tokenizer, for example, contractions
| and abbreviations containing punctuation.
+row
+cell #[+src(gh()) punctuation.py]
+cell
| #[code TOKENIZER_PREFIXES], #[code TOKENIZER_SUFFIXES],
| #[code TOKENIZER_INFIXES] (dicts)
+cell Regular expressions for splitting tokens, e.g. on punctuation.
+row
+cell #[+src(gh()) lex_attrs.py]
+cell #[code LEX_ATTRS] (dict)
+cell
| Functions for setting lexical attributes on tokens, e.g.
| #[code is_punct] or #[code like_num].
+row
+cell #[+src(gh()) tag_map.py]
+cell #[code TAG_MAP] (dict)
+cell
| Dictionary mapping strings in your tag set to
| #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
| tags.
+row
+cell #[+src(gh()) morph_rules.py]
+cell #[code MORPH_RULES] (dict)
+cell
+row
+cell #[+src(gh()) lemmatizer.py]
+cell #[code LOOKUP] (dict)
+cell
| Lookup-based lemmatization table. If more lemmatizer data is
| available, it should live in #[code /lemmatizer/lookup.py].
+row
+cell /lemmatizer
+cell #[code LEMMA_RULES], #[code LEMMA_INDEX], #[code LEMMA_EXC] (dicts)
+cell Lemmatization rules, keyed by part of speech.
+aside("Should I ever update the global data?")
| Reuseable language data is collected as atomic pieces in the root of the
| #[+src(gh("spaCy", "lang")) spacy.lang] package. Often, when a new
| language is added, you'll find a pattern or symbol that's missing. Even
| if it isn't common in other languages, it might be best to add it to the
| shared language data, unless it has some conflicting interpretation. For
| instance, we don't expect to see guillemot quotation symbols
| (#[code »] and #[code «]) in English text. But if we do see
| them, we'd probably prefer the tokenizer to split them off.
+infobox("For languages with non-latin characters")
| In order for the tokenizer to split suffixes, prefixes and infixes, spaCy
| needs to know the language's character set. If the language you're adding
| uses non-latin characters, you might need to add the required character
| classes to the global
| #[+src(gh("spacy", "spacy/lang/char_classes.py")) char_classes.py].
| spaCy uses the #[+a("https://pypi.python.org/pypi/regex/") #[code regex] library]
| to keep this simple and readable. If the language requires very specific
| punctuation rules, you should consider overwriting the default regular
| expressions with your own in the language's #[code Defaults].
+h(3, "stop-words") Stop words
p
| A #[+a("https://en.wikipedia.org/wiki/Stop_words") "stop list"] is a
| classic trick from the early days of information retrieval when search
| was largely about keyword presence and absence. It is still sometimes
| useful today to filter out common words from a bag-of-words model. To
| improve readability, #[code STOP_WORDS] are separated by spaces and
| newlines, and added as a multiline string.
+aside("What does spaCy consider a stop word?")
| There's no particularly principal logic behind what words should be
| added to the stop list. Make a list that you think might be useful
| to people and is likely to be unsurprising. As a rule of thumb, words
| that are very rare are unlikely to be useful stop words.
+code("Example").
STOP_WORDS = set("""
a about above across after afterwards again against all almost alone along
already also although always am among amongst amount an and another any anyhow
anyone anything anyway anywhere are around as at
back be became because become becomes becoming been before beforehand behind
being below beside besides between beyond both bottom but by
""").split())
+infobox("Important note")
| When adding stop words from an online source, always #[strong include the link]
| in a comment. Make sure to #[strong proofread] and double-check the words
| carefully. A lot of the lists available online have been passed around
| for years and often contain mistakes, like unicode errors or random words
| that have once been added for a specific use case, but don't actually
| qualify.
+h(3, "tokenizer-exceptions") Tokenizer exceptions
p
| spaCy's #[+a("/docs/usage/customizing-tokenizer#how-tokenizer-works") tokenization algorithm]
| lets you deal with whitespace-delimited chunks separately. This makes it
| easy to define special-case rules, without worrying about how they
| interact with the rest of the tokenizer. Whenever the key string is
| matched, the special-case rule is applied, giving the defined sequence of
| tokens. You can also attach attributes to the subtokens, covered by your
| special case, such as the subtokens #[code LEMMA] or #[code TAG].
p
| Tokenizer exceptions can be added in the following format:
+code("tokenizer_exceptions.py (excerpt)").
TOKENIZER_EXCEPTIONS = {
"don't": [
{ORTH: "do", LEMMA: "do"},
{ORTH: "n't", LEMMA: "not", TAG: "RB"}]
}
+infobox("Important note")
| If an exception consists of more than one token, the #[code ORTH] values
| combined always need to #[strong match the original string]. The way the
| original string is split up can be pretty arbitrary sometimes – for
| example "gonna" is split into "gon" (lemma "go") nad "na" (lemma "to").
| Because of how the tokenizer works, it's currently not possible to split
| single-letter strings into multiple tokens.
p
| Unambiguous abbreviations, like month names or locations in English,
| should be added to exceptions with a lemma assigned, for example
| #[code {ORTH: "Jan.", LEMMA: "January"}]. Since the exceptions are
| added in Python, you can use custom logic to generate them more
| efficiently and make your data less verbose. How you do this ultimately
| depends on the language. Here's an example of how exceptions for time
| formats like "1a.m." and "1am" are generated in the English
| #[+src(gh("spaCy", "spacy/en/lang/tokenizer_exceptions.py")) tokenizer_exceptions.py]:
+code("tokenizer_exceptions.py (excerpt)").
# use short, internal variable for readability
_exc = {}
for h in range(1, 12 + 1):
for period in ["a.m.", "am"]:
# always keep an eye on string interpolation!
_exc["%d%s" % (h, period)] = [
{ORTH: "%d" % h},
{ORTH: period, LEMMA: "a.m."}]
for period in ["p.m.", "pm"]:
_exc["%d%s" % (h, period)] = [
{ORTH: "%d" % h},
{ORTH: period, LEMMA: "p.m."}]
# only declare this at the bottom
TOKENIZER_EXCEPTIONS = dict(_exc)
+aside("Generating tokenizer exceptions")
| Keep in mind that generating exceptions only makes sense if there's a
| clearly defined and #[strong finite number] of them, like common
| contractions in English. This is not always the case – in Spanish for
| instance, infinitive or imperative reflexive verbs and pronouns are one
| token (e.g. "vestirme"). In cases like this, spaCy shouldn't be
| generating exceptions for #[em all verbs]. Instead, this will be handled
| at a later stage during lemmatization.
p
| When adding the tokenizer exceptions to the #[code Defaults], you can use
| the #[code update_exc()] helper function to merge them with the global
| base exceptions (including one-letter abbreviations and emoticons).
| The function performs a basic check to make sure exceptions are
| provided in the correct format. It can take any number of exceptions
| dicts as its arguments, and will update and overwrite the exception in
| this order. For example, if your language's tokenizer exceptions include
| a custom tokenization pattern for "a.", it will overwrite the base
| exceptions with the language's custom one.
+code("Example").
from ...util import update_exc
BASE_EXCEPTIONS = {"a.": [{ORTH: "a."}], ":)": [{ORTH: ":)"}]}
TOKENIZER_EXCEPTIONS = {"a.": [{ORTH: "a.", LEMMA: "all"}]}
tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
# {"a.": [{ORTH: "a.", LEMMA: "all"}], ":)": [{ORTH: ":)"}]}
//-+aside("About spaCy's custom pronoun lemma")
| Unlike verbs and common nouns, there's no clear base form of a personal
| pronoun. Should the lemma of "me" be "I", or should we normalize person
| as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
| novel symbol, #[code.u-nowrap -PRON-], which is used as the lemma for
| all personal pronouns.
+h(3, "lex-attrs") Lexical attributes
p
| spaCy provides a range of #[+api("token#attributes") #[code Token] attributes]
| that return useful information on that token – for example, whether it's
| uppercase or lowercase, a left or right punctuation mark, or whether it
| resembles a number or email address. Most of these functions, like
| #[code is_lower] or #[code like_url] should be language-independent.
| Others, like #[code like_num] (which includes both digits and number
| words), requires some customisation.
+aside("Best practices")
| Keep in mind that those functions are only intended to be an approximation.
| It's always better to prioritise simplicity and performance over covering
| very specific edge cases.#[br]#[br]
| English number words are pretty simple, because even large numbers
| consist of individual tokens, and we can get away with splitting and
| matching strings against a list. In other languages, like German, "two
| hundred and thirty-four" is one word, and thus one token. Here, it's best
| to match a string against a list of number word fragments (instead of a
| technically almost infinite list of possible number words).
p
| Here's an example from the English
| #[+src(gh("spaCy", "spacy/en/lang/lex_attrs.py")) lex_attrs.py]:
+code("lex_attrs.py").
_num_words = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven',
'eight', 'nine', 'ten', 'eleven', 'twelve', 'thirteen', 'fourteen',
'fifteen', 'sixteen', 'seventeen', 'eighteen', 'nineteen', 'twenty',
'thirty', 'forty', 'fifty', 'sixty', 'seventy', 'eighty', 'ninety',
'hundred', 'thousand', 'million', 'billion', 'trillion', 'quadrillion',
'gajillion', 'bazillion']
def like_num(text):
text = text.replace(',', '').replace('.', '')
if text.isdigit():
return True
if text.count('/') == 1:
num, denom = text.split('/')
if num.isdigit() and denom.isdigit():
return True
if text in _num_words:
return True
return False
LEX_ATTRS = {
LIKE_NUM: like_num
}
p
| By updating the default lexical attributes with a custom #[code LEX_ATTRS]
| dictionary in the language's defaults via
| #[code lex_attr_getters.update(LEX_ATTRS)], only the new custom functions
| are overwritten.
+h(3, "lemmatizer") Lemmatizer
+h(3, "tag-map") Tag map
p
| Most treebanks define a custom part-of-speech tag scheme, striking a
| balance between level of detail and ease of prediction. While it's
| useful to have custom tagging schemes, it's also useful to have a common
| scheme, to which the more specific tags can be related. The tagger can
| learn a tag scheme with any arbitrary symbols. However, you need to
| define how those symbols map down to the
| #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies tag set].
| This is done by providing a tag map.
p
| The keys of the tag map should be #[strong strings in your tag set]. The
| values should be a dictionary. The dictionary must have an entry POS
| whose value is one of the
| #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
| tags. Optionally, you can also include morphological features or other
| token attributes in the tag map as well. This allows you to do simple
| #[+a("/docs/usage/pos-tagging#rule-based-morphology") rule-based morphological analysis].
+code("Example").
from ..symbols import POS, NOUN, VERB, DET
TAG_MAP = {
"NNS": {POS: NOUN, "Number": "plur"},
"VBG": {POS: VERB, "VerbForm": "part", "Tense": "pres", "Aspect": "prog"},
"DT": {POS: DET}
}
+h(3, "morph-rules") Morph rules
+h(2, "testing") Testing the new language tokenizer
+h(2, "vocabulary") Building the vocabulary
p
| spaCy expects that common words will be cached in a
| #[+api("vocab") #[code Vocab]] instance. The vocabulary caches lexical
| features, and makes it easy to use information from unlabelled text
| samples in your models. Specifically, you'll usually want to collect
| word frequencies, and train two types of distributional similarity model:
| Brown clusters, and word vectors. The Brown clusters are used as features
| by linear models, while the word vectors are useful for lexical
| similarity models and deep learning.
+h(3, "word-frequencies") Word frequencies
p
| To generate the word frequencies from a large, raw corpus, you can use the
| #[+src(gh("spacy-dev-resources", "training/word_freqs.py")) word_freqs.py]
| script from the spaCy developer resources. Note that your corpus should
| not be preprocessed (i.e. you need punctuation for example). The
| #[+a("/docs/usage/cli#model") #[code model] command] expects a
| tab-separated word frequencies file with three columns:
+list("numbers")
+item The number of times the word occurred in your language sample.
+item The number of distinct documents the word occurred in.
+item The word itself.
p
| An example word frequencies file could look like this:
+code("es_word_freqs.txt", "text").
6361109 111 Aunque
23598543 111 aunque
10097056 111 claro
193454 111 aro
7711123 111 viene
12812323 111 mal
23414636 111 momento
2014580 111 felicidad
233865 111 repleto
15527 111 eto
235565 111 deliciosos
17259079 111 buena
71155 111 Anímate
37705 111 anímate
33155 111 cuéntanos
2389171 111 cuál
961576 111 típico
p
| You should make sure you use the spaCy tokenizer for your
| language to segment the text for your word frequencies. This will ensure
| that the frequencies refer to the same segmentation standards you'll be
| using at run-time. For instance, spaCy's English tokenizer segments
| "can't" into two tokens. If we segmented the text by whitespace to
| produce the frequency counts, we'll have incorrect frequency counts for
| the tokens "ca" and "n't".
+h(3, "brown-clusters") Training the Brown clusters
p
| spaCy's tagger, parser and entity recognizer are designed to use
| distributional similarity features provided by the
| #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
| You should train a model with between 500 and 1000 clusters. A minimum
| frequency threshold of 10 usually works well.
p
| An example clusters file could look like this:
+code("es_clusters.data", "text").
0000 Vestigial 1
0000 Vesturland 1
0000 Veyreau 1
0000 Veynes 1
0000 Vexilografía 1
0000 Vetrigne 1
0000 Vetónica 1
0000 Asunden 1
0000 Villalambrús 1
0000 Vichuquén 1
0000 Vichtis 1
0000 Vichigasta 1
0000 VAAH 1
0000 Viciebsk 1
0000 Vicovaro 1
0000 Villardeveyo 1
0000 Vidala 1
0000 Videoguard 1
0000 Vedás 1
0000 Videocomunicado 1
0000 VideoCrypt 1
+h(3, "word-vectors") Training the word vectors
p
| #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
| algorithms let you train useful word similarity models from unlabelled
| text. This is a key part of using
| #[+a("/docs/usage/deep-learning") deep learning] for NLP with limited
| labelled data. The vectors are also useful by themselves – they power
| the #[code .similarity()] methods in spaCy. For best results, you should
| pre-process the text with spaCy before training the Word2vec model. This
| ensures your tokenization will match.
p
| You can use our
| #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
| which pre-processes the text with your language-specific tokenizer and
| trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
| The #[code vectors.bin] file should consist of one word and vector per line.
+h(2, "model-directory") Setting up a model directory
p
| Once you've collected the word frequencies, Brown clusters and word
| vectors files, you can use the
| #[+a("/docs/usage/cli#model") #[code model] command] to create a data
| directory:
+code(false, "bash").
python -m spacy model [lang] [model_dir] [freqs_data] [clusters_data] [vectors_data]
+aside-code("your_data_directory", "yaml").
├── vocab/
| ├── lexemes.bin # via nlp.vocab.dump(path)
| ├── strings.json # via nlp.vocab.strings.dump(file_)
| └── oov_prob # optional
├── pos/ # optional
| ├── model # via nlp.tagger.model.dump(path)
| └── config.json # via Langage.train
├── deps/ # optional
| ├── model # via nlp.parser.model.dump(path)
| └── config.json # via Langage.train
└── ner/ # optional
├── model # via nlp.entity.model.dump(path)
└── config.json # via Langage.train
p
| This creates a spaCy data directory with a vocabulary model, ready to be
| loaded. By default, the command expects to be able to find your language
| class using #[code spacy.util.get_lang_class(lang_id)].
+h(2, "train-tagger-parser") Training the tagger and parser
p
| You can now train the model using a corpus for your language annotated
| with #[+a("http://universaldependencies.org/") Universal Dependencies].
| If your corpus uses the
| #[+a("http://universaldependencies.org/docs/format.html") CoNLL-U] format,
| i.e. files with the extension #[code .conllu], you can use the
| #[+a("/docs/usage/cli#convert") #[code convert] command] to convert it to
| spaCy's #[+a("/docs/api/annotation#json-input") JSON format] for training.
p
| Once you have your UD corpus transformed into JSON, you can train your
| model use the using spaCy's
| #[+a("/docs/usage/cli#train") #[code train] command]:
+code(false, "bash").
python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n_iter] [--parser_L1] [--no_tagger] [--no_parser] [--no_ner]
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