--- title: Adding Languages next: /usage/training menu: - ['Language Data', 'language-data'] - ['Testing', 'testing'] - ['Training', 'training'] --- 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. > #### Working on spaCy's source > > To add a new language to spaCy, you'll need to **modify the library's code**. > The easiest way to do this is to clone the > [repository](https://github.com/explosion/spaCy/tree/master/) and **build > spaCy from source**. For more information on this, see the > [installation guide](/usage). Unlike spaCy's core, which is mostly written in > Cython, all language data is stored in regular Python files. This means that > you won't have to rebuild anything in between – you can simply make edits and > reload spaCy to test them.
Obviously, there are lots of ways you can organize your code when you implement your own language data. This guide will focus on how it's done within spaCy. For full language support, you'll need to create a `Language` subclass, define custom **language data**, like a stop list and tokenizer exceptions and test the new tokenizer. Once the language is set up, you can **build the vocabulary**, including word frequencies, Brown clusters and word vectors. Finally, you can **train the tagger and parser**, and save the model to a directory. For some languages, you may also want to develop a solution for lemmatization and morphological analysis.
- [Language data 101](#101) - [The Language subclass](#language-subclass) - [Stop words](#stop-words) - [Tokenizer exceptions](#tokenizer-exceptions) - [Norm exceptions](#norm-exceptions) - [Lexical attributes](#lex-attrs) - [Syntax iterators](#syntax-iterators) - [Lemmatizer](#lemmatizer) - [Tag map](#tag-map) - [Morph rules](#morph-rules) - [Testing the language](#testing) - [Training](#training) ## Language data {#language-data} import LanguageData101 from 'usage/101/\_language-data.md' The individual components **expose variables** that can be imported within a language module, and added to the language's `Defaults`. Some components, like the punctuation rules, usually don't need much customization and can be imported from the global rules. Others, like the tokenizer and norm exceptions, are very specific and will make a big difference to spaCy's performance on the particular language and training a language model. | Variable | Type | Description | | ---------------------- | ----- | ---------------------------------------------------------------------------------------------------------- | | `STOP_WORDS` | set | Individual words. | | `TOKENIZER_EXCEPTIONS` | dict | Keyed by strings mapped to list of one dict per token with token attributes. | | `TOKEN_MATCH` | regex | Regexes to match complex tokens, e.g. URLs. | | `NORM_EXCEPTIONS` | dict | Keyed by strings, mapped to their norms. | | `TOKENIZER_PREFIXES` | list | Strings or regexes, usually not customized. | | `TOKENIZER_SUFFIXES` | list | Strings or regexes, usually not customized. | | `TOKENIZER_INFIXES` | list | Strings or regexes, usually not customized. | | `LEX_ATTRS` | dict | Attribute ID mapped to function. | | `SYNTAX_ITERATORS` | dict | Iterator ID mapped to function. Currently only supports `'noun_chunks'`. | | `TAG_MAP` | dict | Keyed by strings mapped to [Universal Dependencies](http://universaldependencies.org/u/pos/all.html) tags. | | `MORPH_RULES` | dict | Keyed by strings mapped to a dict of their morphological features. | > #### Should I ever update the global data? > > Reusable language data is collected as atomic pieces in the root of the > [`spacy.lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang) > module. 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 (`»` and `«`) in English text. But if we do see them, we'd probably > prefer the tokenizer to split them off. 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 define the required character classes in the global [`char_classes.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/char_classes.py). For efficiency, spaCy uses hard-coded unicode ranges to define character classes, the definitions of which can be found on [Wikipedia](https://en.wikipedia.org/wiki/Unicode_block). If the language requires very specific punctuation rules, you should consider overwriting the default regular expressions with your own in the language's `Defaults`. ### Creating a language subclass {#language-subclass} Language-specific code and resources should be organized into a sub-package of spaCy, named according to the language's [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). For instance, code and resources specific to Spanish are placed into a directory `spacy/lang/es`, which can be imported as `spacy.lang.es`. To get started, you can check out the [existing languages](https://github.com/explosion/spacy/tree/master/spacy/lang). Here's what the class could look like: ```python ### __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 # Create Defaults class in the module scope (necessary for pickling!) class XxxxxDefaults(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 = STOP_WORDS # Create actual Language class class Xxxxx(Language): lang = "xx" # Language ISO code Defaults = XxxxxDefaults # Override defaults # Set default export – this allows the language class to be lazy-loaded __all__ = ["Xxxxx"] ``` Some languages contain large volumes of custom data, like lemmatizer lookup tables, or complex regular expression that are expensive to compute. As of spaCy v2.0, `Language` classes are not imported on initialization 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 [`util.get_lang_class`](/api/top-level#util.get_lang_class) helper function with the two-letter language code as its argument. ### Stop words {#stop-words} A ["stop list"](https://en.wikipedia.org/wiki/Stop_words) 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, `STOP_WORDS` are separated by spaces and newlines, and added as a multiline string. > #### What does spaCy consider a stop word? > > There's no particularly principled 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. ```python ### 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()) ``` When adding stop words from an online source, always **include the link** in a comment. Make sure to **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. ### Tokenizer exceptions {#tokenizer-exceptions} spaCy's [tokenization algorithm](/usage/linguistic-features#how-tokenizer-works) 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. Tokenizer exceptions can be added in the following format: ```python ### tokenizer_exceptions.py (excerpt) TOKENIZER_EXCEPTIONS = { "don't": [ {ORTH: "do"}, {ORTH: "n't", NORM: "not"}] } ``` If an exception consists of more than one token, the `ORTH` values combined always need to **match the original string**. The way the original string is split up can be pretty arbitrary sometimes – for example `"gonna"` is split into `"gon"` (norm "going") and `"na"` (norm "to"). Because of how the tokenizer works, it's currently not possible to split single-letter strings into multiple tokens. > #### Generating tokenizer exceptions > > Keep in mind that generating exceptions only makes sense if there's a clearly > defined and **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 _all verbs_. > Instead, this will be handled at a later stage after part-of-speech tagging > and lemmatization. When adding the tokenizer exceptions to the `Defaults`, you can use the [`update_exc`](/api/top-level#util.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. ```python ### Example from ...util import update_exc BASE_EXCEPTIONS = {"a.": [{ORTH: "a."}], ":)": [{ORTH: ":)"}]} TOKENIZER_EXCEPTIONS = {"a.": [{ORTH: "a.", NORM: "all"}]} tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS) # {"a.": [{ORTH: "a.", NORM: "all"}], ":)": [{ORTH: ":)"}]} ``` ### Norm exceptions {#norm-exceptions new="2"} In addition to `ORTH`, tokenizer exceptions can also set a `NORM` attribute. This is useful to specify a normalized version of the token – for example, the norm of "n't" is "not". By default, a token's norm equals its lowercase text. If the lowercase spelling of a word exists, norms should always be in lowercase. > #### Norms vs. lemmas > > ```python > doc = nlp("I'm gonna realise") > norms = [token.norm_ for token in doc] > lemmas = [token.lemma_ for token in doc] > assert norms == ["i", "am", "going", "to", "realize"] > assert lemmas == ["i", "be", "go", "to", "realise"] > ``` spaCy usually tries to normalize words with different spellings to a single, common spelling. This has no effect on any other token attributes, or tokenization in general, but it ensures that **equivalent tokens receive similar representations**. This can improve the model's predictions on words that weren't common in the training data, but are equivalent to other words – for example, "realize" and "realize", or "thx" and "thanks". Similarly, spaCy also includes [global base norms](https://github.com/explosion/spaCy/tree/master/spacy/lang/norm_exceptions.py) for normalizing different styles of quotation marks and currency symbols. Even though `$` and `€` are very different, spaCy normalizes them both to `$`. This way, they'll always be seen as similar, no matter how common they were in the training data. Norm exceptions can be provided as a simple dictionary. For more examples, see the English [`norm_exceptions.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/en/norm_exceptions.py). ```python ### Example NORM_EXCEPTIONS = { "cos": "because", "fav": "favorite", "accessorise": "accessorize", "accessorised": "accessorized" } ``` To add the custom norm exceptions lookup table, you can use the `add_lookups()` helper functions. It takes the default attribute getter function as its first argument, plus a variable list of dictionaries. If a string's norm is found in one of the dictionaries, that value is used – otherwise, the default function is called and the token is assigned its default norm. ```python lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM], NORM_EXCEPTIONS, BASE_NORMS) ``` The order of the dictionaries is also the lookup order – so if your language's norm exceptions overwrite any of the global exceptions, they should be added first. Also note that the tokenizer exceptions will always have priority over the attribute getters. ### Lexical attributes {#lex-attrs new="2"} spaCy provides a range of [`Token` attributes](/api/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 `is_lower` or `like_url` should be language-independent. Others, like `like_num` (which includes both digits and number words), requires some customization. > #### Best practices > > Keep in mind that those functions are only intended to be an approximation. > It's always better to prioritize simplicity and performance over covering very > specific edge cases. > > 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). Here's an example from the English [`lex_attrs.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/en/lex_attrs.py): ```python ### 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.lower() in _num_words: return True return False LEX_ATTRS = { LIKE_NUM: like_num } ``` By updating the default lexical attributes with a custom `LEX_ATTRS` dictionary in the language's defaults via `lex_attr_getters.update(LEX_ATTRS)`, only the new custom functions are overwritten. ### Syntax iterators {#syntax-iterators} Syntax iterators are functions that compute views of a `Doc` object based on its syntax. At the moment, this data is only used for extracting [noun chunks](/usage/linguistic-features#noun-chunks), which are available as the [`Doc.noun_chunks`](/api/doc#noun_chunks) property. Because base noun phrases work differently across languages, the rules to compute them are part of the individual language's data. If a language does not include a noun chunks iterator, the property won't be available. For examples, see the existing syntax iterators: > #### Noun chunks example > > ```python > doc = nlp("A phrase with another phrase occurs.") > chunks = list(doc.noun_chunks) > assert chunks[0].text == "A phrase" > assert chunks[1].text == "another phrase" > ``` | Language | Code | Source | | -------- | ---- | ----------------------------------------------------------------------------------------------------------------- | | English | `en` | [`lang/en/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/en/syntax_iterators.py) | | German | `de` | [`lang/de/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/de/syntax_iterators.py) | | French | `fr` | [`lang/fr/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/fr/syntax_iterators.py) | | Spanish | `es` | [`lang/es/syntax_iterators.py`](https://github.com/explosion/spaCy/tree/master/spacy/lang/es/syntax_iterators.py) | ### Lemmatizer {#lemmatizer new="2"} As of v2.0, spaCy supports simple lookup-based lemmatization. This is usually the quickest and easiest way to get started. The data is stored in a dictionary mapping a string to its lemma. To determine a token's lemma, spaCy simply looks it up in the table. Here's an example from the Spanish language data: ```json ### lang/es/lemma_lookup.json (excerpt) { "aba": "abar", "ababa": "abar", "ababais": "abar", "ababan": "abar", "ababanes": "ababán", "ababas": "abar", "ababoles": "ababol", "ababábites": "ababábite" } ``` #### Adding JSON resources {#lemmatizer-resources new="2.2"} As of v2.2, resources for the lemmatizer are stored as JSON and loaded via the new [`Lookups`](/api/lookups) class. This allows easier access to the data, serialization with the models and file compression on disk (so your spaCy installation is smaller). Resource files can be provided via the `resources` attribute on the custom language subclass. All paths are relative to the language data directory, i.e. the directory the language's `__init__.py` is in. ```python resources = { "lemma_lookup": "lemmatizer/lemma_lookup.json", "lemma_rules": "lemmatizer/lemma_rules.json", "lemma_index": "lemmatizer/lemma_index.json", "lemma_exc": "lemmatizer/lemma_exc.json", } ``` ### Tag map {#tag-map} 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 [Universal Dependencies tag set](http://universaldependencies.org/u/pos/all.html). This is done by providing a tag map. The keys of the tag map should be **strings in your tag set**. The values should be a dictionary. The dictionary must have an entry POS whose value is one of the [Universal Dependencies](http://universaldependencies.org/u/pos/all.html) tags. Optionally, you can also include morphological features or other token attributes in the tag map as well. This allows you to do simple [rule-based morphological analysis](/usage/linguistic-features#rule-based-morphology). ```python ### 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} } ``` ### Morph rules {#morph-rules} The morphology rules let you set token attributes such as lemmas, keyed by the extended part-of-speech tag and token text. The morphological features and their possible values are language-specific and based on the [Universal Dependencies scheme](http://universaldependencies.org). ```python ### Example from ..symbols import LEMMA MORPH_RULES = { "VBZ": { "am": {LEMMA: "be", "VerbForm": "Fin", "Person": "One", "Tense": "Pres", "Mood": "Ind"}, "are": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"}, "is": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"}, "'re": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"}, "'s": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"} } } ``` In the example of `"am"`, the attributes look like this: | Attribute | Description | | ------------------- | ------------------------------------------------------------------------------------------------------------------------------ | | `LEMMA: "be"` | Base form, e.g. "to be". | | `"VerbForm": "Fin"` | Finite verb. Finite verbs have a subject and can be the root of an independent clause – "I am." is a valid, complete sentence. | | `"Person": "One"` | First person, i.e. "**I** am". | | `"Tense": "Pres"` | Present tense, i.e. actions that are happening right now or actions that usually happen. | | `"Mood": "Ind"` | Indicative, i.e. something happens, has happened or will happen (as opposed to imperative or conditional). | The morphological attributes are currently **not all used by spaCy**. Full integration is still being developed. In the meantime, it can still be useful to add them, especially if the language you're adding includes important distinctions and special cases. This ensures that as soon as full support is introduced, your language will be able to assign all possible attributes. ## Testing the new language {#testing} Before using the new language or submitting a [pull request](https://github.com/explosion/spaCy/pulls) to spaCy, you should make sure it works as expected. This is especially important if you've added custom regular expressions for token matching or punctuation – you don't want to be causing regressions. spaCy uses the [pytest framework](https://docs.pytest.org/en/latest/) for testing. For more details on how the tests are structured and best practices for writing your own tests, see our [tests documentation](https://github.com/explosion/spaCy/tree/master/spacy/tests). ### Writing language-specific tests {#testing-custom} It's recommended to always add at least some tests with examples specific to the language. Language tests should be located in [`tests/lang`](https://github.com/explosion/spaCy/tree/master/spacy/tests/lang) in a directory named after the language ID. You'll also need to create a fixture for your tokenizer in the [`conftest.py`](https://github.com/explosion/spaCy/tree/master/spacy/tests/conftest.py). Always use the [`get_lang_class`](/api/top-level#util.get_lang_class) helper function within the fixture, instead of importing the class at the top of the file. This will load the language data only when it's needed. (Otherwise, _all data_ would be loaded every time you run a test.) ```python @pytest.fixture def en_tokenizer(): return util.get_lang_class("en").Defaults.create_tokenizer() ``` When adding test cases, always [`parametrize`](https://github.com/explosion/spaCy/tree/master/spacy/tests#parameters) them – this will make it easier for others to add more test cases without having to modify the test itself. You can also add parameter tuples, for example, a test sentence and its expected length, or a list of expected tokens. Here's an example of an English tokenizer test for combinations of punctuation and abbreviations: ```python ### Example test @pytest.mark.parametrize('text,length', [ ("The U.S. Army likes Shock and Awe.", 8), ("U.N. regulations are not a part of their concern.", 10), ("“Isn't it?”", 6)]) def test_en_tokenizer_handles_punct_abbrev(en_tokenizer, text, length): tokens = en_tokenizer(text) assert len(tokens) == length ``` ## Training a language model {#training} Much of spaCy's functionality requires models to be trained from labeled data. For instance, in order to use the named entity recognizer, you need to first train a model on text annotated with examples of the entities you want to recognize. The parser, part-of-speech tagger and text categorizer all also require models to be trained from labeled examples. The word vectors, word probabilities and word clusters also require training, although these can be trained from unlabeled text, which tends to be much easier to collect. ### Creating a vocabulary file {#vocab-file} spaCy expects that common words will be cached in a [`Vocab`](/api/vocab) instance. The vocabulary caches lexical features. spaCy loads the vocabulary from binary data, in order to keep loading efficient. The easiest way to save out a new binary vocabulary file is to use the `spacy init-model` command, which expects a JSONL file with words and their lexical attributes. See the docs on the [vocab JSONL format](/api/annotation#vocab-jsonl) for details. #### Training the word vectors {#word-vectors} [Word2vec](https://en.wikipedia.org/wiki/Word2vec) and related algorithms let you train useful word similarity models from unlabeled text. This is a key part of using deep learning for NLP with limited labeled data. The vectors are also useful by themselves – they power the `.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. You can use our [word vectors training script](https://github.com/explosion/spacy/tree/master/bin/train_word_vectors.py), which pre-processes the text with your language-specific tokenizer and trains the model using [Gensim](https://radimrehurek.com/gensim/). The `vectors.bin` file should consist of one word and vector per line. ```python https://github.com/explosion/spacy/tree/master/bin/train_word_vectors.py ``` If you don't have a large sample of text available, you can also convert word vectors produced by a variety of other tools into spaCy's format. See the docs on [converting word vectors](/usage/vectors-similarity#converting) for details. ### Creating or converting a training corpus {#training-corpus} The easiest way to train spaCy's tagger, parser, entity recognizer or text categorizer is to use the [`spacy train`](/api/cli#train) command-line utility. In order to use this, you'll need training and evaluation data in the [JSON format](/api/annotation#json-input) spaCy expects for training. You can now train the model using a corpus for your language annotated with If your data is in one of the supported formats, the easiest solution might be to use the [`spacy convert`](/api/cli#convert) command-line utility. This supports several popular formats, including the IOB format for named entity recognition, the JSONL format produced by our annotation tool [Prodigy](https://prodi.gy), and the [CoNLL-U](http://universaldependencies.org/docs/format.html) format used by the [Universal Dependencies](http://universaldependencies.org/) corpus. One thing to keep in mind is that spaCy expects to train its models from **whole documents**, not just single sentences. If your corpus only contains single sentences, spaCy's models will never learn to expect multi-sentence documents, leading to low performance on real text. To mitigate this problem, you can use the `-N` argument to the `spacy convert` command, to merge some of the sentences into longer pseudo-documents. ### Training the tagger and parser {#train-tagger-parser} Once you have your training and evaluation data in the format spaCy expects, you can train your model use the using spaCy's [`train`](/api/cli#train) command. Note that training statistical models still involves a degree of trial-and-error. You may need to tune one or more settings, also called "hyper-parameters", to achieve optimal performance. See the [usage guide on training](/usage/training#tagger-parser) for more details.