spaCy 101 – Everything you need to know
Whether you're new to spaCy, or just want to brush up on some NLP basics and implementation details – this page should have you covered. Each section will explain one of spaCy's features in simple terms and with examples or illustrations. Some sections will also reappear across the usage guides as a quick introcution.
spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python.
If you're working with a lot of text, you'll eventually want to know more about it. For example, what's it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other?
spaCy is designed specifically for production use and helps you build applications that process and "understand" large volumes of text. It can be used to build information extraction or natural language understanding systems, or to pre-process text for deep learning.
What spaCy isn't
- spaCy is not a platform or "an API". Unlike a platform, spaCy does not provide a software as a service, or a web application. It's an open-source library designed to help you build NLP applications, not a consumable service.
- spaCy is not an out-of-the-box chat bot engine. While spaCy can be used to power conversational applications, it's not designed specifically for chat bots, and only provides the underlying text processing capabilities.
- spaCy is not research software. It's is built on the latest research, but it's designed to get things done. This leads to fairly different design decisions than NLTK or CoreNLP, which were created as platforms for teaching and research. The main difference is that spaCy is integrated and opinionated. spaCy tries to avoid asking the user to choose between multiple algorithms that deliver equivalent functionality. Keeping the menu small lets spaCy deliver generally better performance and developer experience.
- spaCy is not a company. It's an open-source library. Our company publishing spaCy and other software is called Explosion AI.
In the documentation, you'll come across mentions of spaCy's features and capabilities. Some of them refer to linguistic concepts, while others are related to more general machine learning functionality.
|Tokenization||Segmenting text into words, punctuations marks etc.|
|Part-of-speech (POS) Tagging||Assigning word types to tokens, like verb or noun.|
|Dependency Parsing||Assigning syntactic dependency labels, describing the relations between individual tokens, like subject or object.|
|Lemmatization||Assigning the base forms of words. For example, the lemma of "was" is "be", and the lemma of "rats" is "rat".|
|Sentence Boundary Detection (SBD)||Finding and segmenting individual sentences.|
|Named Entity Recongition (NER)||Labelling named "real-world" objects, like persons, companies or locations.|
|Rule-based Matching||Finding sequences of tokens based on their texts and linguistic annotations, similar to regular expressions.|
|Similarity||Comparing words, text spans and documents and how similar they are to each other.|
|Training||Updating and improving a statistical model's predictions.|
|Serialization||Saving objects to files or byte strings.|
spaCy provides a variety of linguistic annotations to give you insights into a text's grammatical structure. This includes the word types, like the parts of speech, and how the words are related to each other. For example, if you're analysing text, it makes a huge difference whether a noun is the subject of a sentence, or the object – or whether "google" is used as a verb, or refers to the website or company in a specific context.
Once you've downloaded and installed a model, you can load it via
spacy.load() . This will return a
Language object contaning all components and data needed to process text. We usually call it
nlp. Calling the
nlp object on a string of text will return a processed
import spacy nlp = spacy.load('en') doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
Even though a
Doc is processed – e.g. split into individual words and annotated – it still holds all information of the original text, like whitespace characters. You can always get the offset of a token into the
original string, or reconstruct the original by joining the tokens and their
trailing whitespace. This way, you'll never lose any information
when processing text with spaCy.
During processing, spaCy first tokenizes the text, i.e. segments it into words, punctuation and so on. This is done by applying
rules specific to each language. For example, punctuation at the end of a
sentence should be split off – whereas "U.K." should remain one token. Each
Doc consists of individual tokens, and we can simply iterate over them:
for token in doc: print(token.text)
Fist, the raw text is split on whitespace characters, similar to
text.split(' '). Then, the tokenizer processes the text from left to right. On each substring, it performs two checks:
- Does the substring match a tokenizer exception rule? For example, "don't" does not contain whitespace, but should be split into two tokens, "do" and "n't", while "U.K." should always remain one token.
- Can a prefix, suffix or infix be split off? For example punctuation like commas, periods, hyphens or quotes.
If there's a match, the rule is applied and the tokenizer continues its loop, starting with the newly split substrings. This way, spaCy can split complex, nested tokens like combinations of abbreviations and multiple punctuation marks.
While punctuation rules are usually pretty general, tokenizer exceptions
strongly depend on the specifics of the individual language. This is why each available language has its own subclass like
German, that loads in lists of hard-coded data and exception rules.
Part-of-speech tags and dependencies
After tokenization, spaCy can also parse and tag a given
Doc. This is where the statistical model comes in, which enables spaCy to make a prediction of which tag or label most likely applies in this context. A model consists of binary data and is
produced by showing a system enough examples for it to make predictions
that generalise across the language – for example, a word following "the"
in English is most likely a noun.
Linguistic annotations are available as
Token attributes . Like many NLP libraries, spaCy encodes all strings to hash values to reduce memory usage and improve efficiency. So to get the readable string representation of an attribute, we need to add an underscore
_ to its name:
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion') for token in doc: print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_, token.shape_, token.is_alpha, token.is_stop)
Using spaCy's built-in displaCy visualizer, here's what our example sentence and its dependencies look like:
A named entity is a "real-world object" that's assigned a name – for example, a person, a country, a product or a book title. spaCy can recognise various types of named entities in a document, by asking the model for a prediction. Because models are statistical and strongly depend on the examples they were trained on, this doesn't always work perfectly and might need some tuning later, depending on your use case.
Named entities are available as the
ents property of a
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion') for ent in doc.ents: print(ent.text, ent.start_char, ent.end_char, ent.label_)
|Apple||Companies, agencies, institutions.|
|U.K.||Geopolitical entity, i.e. countries, cities, states.|
|$1 billion||Monetary values, including unit.|
Using spaCy's built-in displaCy visualizer, here's what our example sentence and its named entities look like:
Word vectors and similarity
spaCy is able to compare two objects, and make a prediction of how similar they are. Predicting similarity is useful for building recommendation systems or flagging duplicates. For example, you can suggest a user content that's similar to what they're currently looking at, or label a support ticket as a duplicate if it's very similar to an already existing one.
Token comes with a
.similarity() method that lets you compare it with another object, and determine the similarity. Of course
similarity is always subjective – whether "dog" and "cat" are similar
really depends on how you're looking at it. spaCy's similarity model
usually assumes a pretty general-purpose definition of similarity.
tokens = nlp(u'dog cat banana') for token1 in tokens: for token2 in tokens: print(token1.similarity(token2))
In this case, the model's predictions are pretty on point. A dog is very
similar to a cat, whereas a banana is not very similar to either of them.
Identical tokens are obviously 100% similar to each other (just not always exactly
1.0, because of vector math and floating point imprecisions).
Similarity is determined by comparing word vectors or "word embeddings", multi-dimensional meaning representations of a word. Word vectors can be generated using an algorithm like word2vec. Most of spaCy's default models come with 300-dimensional vectors that look like this:
array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01, 3.28450017e-02, -4.19569999e-01, 7.20689967e-02, -3.74760002e-01, 5.74599989e-02, -1.24009997e-02, 5.29489994e-01, -5.23800015e-01, -1.97710007e-01, -3.41470003e-01, 5.33169985e-01, -2.53309999e-02, 1.73800007e-01, 1.67720005e-01, 8.39839995e-01, 5.51070012e-02, 1.05470002e-01, 3.78719985e-01, 2.42750004e-01, 1.47449998e-02, 5.59509993e-01, 1.25210002e-01, -6.75960004e-01, 3.58420014e-01, -4.00279984e-02, 9.59490016e-02, -5.06900012e-01, -8.53179991e-02, 1.79800004e-01, 3.38669986e-01, 1.32300004e-01, 3.10209990e-01, 2.18779996e-01, 1.68530002e-01, 1.98740005e-01, -5.73849976e-01, -1.06490001e-01, 2.66689986e-01, 1.28380001e-01, -1.28030002e-01, -1.32839993e-01, 1.26570001e-01, 8.67229998e-01, 9.67210010e-02, 4.83060002e-01, 2.12709993e-01, -5.49900010e-02, -8.24249983e-02, 2.24079996e-01, 2.39749998e-01, -6.22599982e-02, 6.21940017e-01, -5.98999977e-01, 4.32009995e-01, 2.81430006e-01, 3.38420011e-02, -4.88150001e-01, -2.13589996e-01, 2.74010003e-01, 2.40950003e-01, 4.59500015e-01, -1.86049998e-01, -1.04970002e+00, -9.73049998e-02, -1.89080000e-01, -7.09290028e-01, 4.01950002e-01, -1.87680006e-01, 5.16870022e-01, 1.25200003e-01, 8.41499984e-01, 1.20970003e-01, 8.82389992e-02, -2.91959997e-02, 1.21510006e-03, 5.68250008e-02, -2.74210006e-01, 2.55640000e-01, 6.97930008e-02, -2.22580001e-01, -3.60060006e-01, -2.24020004e-01, -5.36990017e-02, 1.20220006e+00, 5.45350015e-01, -5.79980016e-01, 1.09049998e-01, 4.21669990e-01, 2.06619993e-01, 1.29360005e-01, -4.14570011e-02, -6.67770028e-01, 4.04670000e-01, -1.52179999e-02, -2.76400000e-01, -1.56110004e-01, -7.91980028e-02, 4.00369987e-02, -1.29439995e-01, -2.40900001e-04, -2.67850012e-01, -3.81150007e-01, -9.72450018e-01, 3.17259997e-01, -4.39509988e-01, 4.19340014e-01, 1.83530003e-01, -1.52600005e-01, -1.08080000e-01, -1.03579998e+00, 7.62170032e-02, 1.65189996e-01, 2.65259994e-04, 1.66160002e-01, -1.52810007e-01, 1.81229994e-01, 7.02740014e-01, 5.79559989e-03, 5.16639985e-02, -5.97449988e-02, -2.75510013e-01, -3.90489995e-01, 6.11319989e-02, 5.54300010e-01, -8.79969969e-02, -4.16810006e-01, 3.28260005e-01, -5.25489986e-01, -4.42880005e-01, 8.21829960e-03, 2.44859993e-01, -2.29819998e-01, -3.49810004e-01, 2.68940002e-01, 3.91660005e-01, -4.19039994e-01, 1.61909997e-01, -2.62630010e+00, 6.41340017e-01, 3.97430003e-01, -1.28680006e-01, -3.19460005e-01, -2.56330013e-01, -1.22199997e-01, 3.22750002e-01, -7.99330026e-02, -1.53479993e-01, 3.15050006e-01, 3.05909991e-01, 2.60120004e-01, 1.85530007e-01, -2.40429997e-01, 4.28860001e-02, 4.06219989e-01, -2.42559999e-01, 6.38700008e-01, 6.99829996e-01, -1.40430003e-01, 2.52090007e-01, 4.89840001e-01, -6.10670000e-02, -3.67659986e-01, -5.50890028e-01, -3.82649988e-01, -2.08430007e-01, 2.28320003e-01, 5.12179971e-01, 2.78679997e-01, 4.76520002e-01, 4.79510017e-02, -3.40079993e-01, -3.28729987e-01, -4.19669986e-01, -7.54989982e-02, -3.89539987e-01, -2.96219997e-02, -3.40700001e-01, 2.21699998e-01, -6.28560036e-02, -5.19029975e-01, -3.77739996e-01, -4.34770016e-03, -5.83010018e-01, -8.75459984e-02, -2.39289999e-01, -2.47109994e-01, -2.58870006e-01, -2.98940003e-01, 1.37150005e-01, 2.98919994e-02, 3.65439989e-02, -4.96650010e-01, -1.81600004e-01, 5.29389977e-01, 2.19919994e-01, -4.45140004e-01, 3.77979994e-01, -5.70620000e-01, -4.69460003e-02, 8.18059966e-02, 1.92789994e-02, 3.32459986e-01, -1.46200001e-01, 1.71560004e-01, 3.99809986e-01, 3.62170011e-01, 1.28160000e-01, 3.16439986e-01, 3.75690013e-01, -7.46899992e-02, -4.84800003e-02, -3.14009994e-01, -1.92860007e-01, -3.12940001e-01, -1.75529998e-02, -1.75139993e-01, -2.75870003e-02, -1.00000000e+00, 1.83870003e-01, 8.14339995e-01, -1.89129993e-01, 5.09989977e-01, -9.19600017e-03, -1.92950002e-03, 2.81890005e-01, 2.72470005e-02, 4.34089988e-01, -5.49669981e-01, -9.74259973e-02, -2.45399997e-01, -1.72030002e-01, -8.86500031e-02, -3.02980006e-01, -1.35910004e-01, -2.77649999e-01, 3.12860007e-03, 2.05559999e-01, -1.57720000e-01, -5.23079991e-01, -6.47010028e-01, -3.70139986e-01, 6.93930015e-02, 1.14009999e-01, 2.75940001e-01, -1.38750002e-01, -2.72680014e-01, 6.68910027e-01, -5.64539991e-02, 2.40170002e-01, -2.67300010e-01, 2.98599988e-01, 1.00830004e-01, 5.55920005e-01, 3.28489989e-01, 7.68579990e-02, 1.55279994e-01, 2.56359994e-01, -1.07720003e-01, -1.23590000e-01, 1.18270002e-01, -9.90289971e-02, -3.43279988e-01, 1.15019999e-01, -3.78080010e-01, -3.90120000e-02, -3.45930010e-01, -1.94040000e-01, -3.35799992e-01, -6.23340011e-02, 2.89189994e-01, 2.80319989e-01, -5.37410021e-01, 6.27939999e-01, 5.69549985e-02, 6.21469975e-01, -2.52819985e-01, 4.16700006e-01, -1.01079997e-02, -2.54339993e-01, 4.00029987e-01, 4.24320012e-01, 2.26720005e-01, 1.75530002e-01, 2.30489999e-01, 2.83230007e-01, 1.38820007e-01, 3.12180002e-03, 1.70570001e-01, 3.66849989e-01, 2.52470002e-03, -6.40089989e-01, -2.97650009e-01, 7.89430022e-01, 3.31680000e-01, -1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32)
.vector attribute will return an object's vector.
Span.vector will default to an average of their token vectors. You can also check if a token has a vector
assigned, and get the L2 norm, which can be used to normalise
tokens = nlp(u'dog cat banana sasquatch') for token in tokens: print(token.text, token.has_vector, token.vector_norm, token.is_oov)
|Text||Has vector||Vector norm||OOV|
The words "dog", "cat" and "banana" are all pretty common in English, so
they're part of the model's vocabulary, and come with a vector. The word
"sasquatch" on the other hand is a lot less common and out-of-vocabulary – so its vector representation consists of 300 dimensions of
0, which means it's practically nonexistent.
If your application will benefit from a large vocabulary with more vectors, you should consider using one of the larger models instead of the default, smaller ones, which usually come with a clipped vocabulary.
When you call
nlp on a text, spaCy first tokenizes the text to produce a
Doc object. The
Doc is then processed in several different steps – this is also referred to as the processing pipeline. The pipeline used by the default models consists of a tensorizer, a tagger, a parser and an entity recognizer. Each pipeline component returns the processed
Doc, which is then passed on to the next component.
|tokenizer||Segment text into tokens.|
|tensorizer||Create feature representation tensor for |
|tagger||Assign part-of-speech tags.|
|parser|| ||Assign dependency labels.|
|ner||Detect and label named entities.|
The processing pipeline always depends on the statistical model and its capabilities. For example, a pipeline can only include an entity recognizer component if the model includes data to make predictions of entity labels. This is why each model will specify the pipeline to use in its meta data, as a simple list containing the component names:
"pipeline": ["tensorizer", "tagger", "parser", "ner"]
Although you can mix and match pipeline components, their order and combination is usually important. Some components may require certain modifications on the
Doc to process it. For example, the default pipeline first applies the tensorizer, which
pre-processes the doc and encodes its internal meaning representations as an array of floats, also called a tensor. This includes the tokens and their context, which is required for the next component, the tagger, to make predictions of the
part-of-speech tags. Because spaCy's models are neural network models, they only "speak" tensors and expect the input
Doc to have a
Vocab, hashes and lexemes
Whenever possible, spaCy tries to store data in a vocabulary, the
Vocab , that will be shared by multiple documents. To save memory, spaCy also encodes all strings to hash values – in this case for example, "coffee" has the hash
3197928453018144401. Entity labels like "ORG" and part-of-speech tags like "VERB" are also encoded. Internally,
spaCy only "speaks" in hash values.
If you process lots of documents containing the word "coffee" in all
kinds of different contexts, storing the exact string "coffee" every time
would take up way too much space. So instead, spaCy hashes the string and stores it in the
StringStore . You can think of the
StringStore as a lookup table that works in both directions – you can look up a string to get its hash, or a hash to get its string:
doc = nlp(u'I like coffee') assert doc.vocab.strings[u'coffee'] == 3197928453018144401 assert doc.vocab.strings == u'coffee'
Now that all strings are encoded, the entries in the vocabulary don't need to include the word text themselves. Instead, they can look it up in the
StringStore via its hash value. Each entry in the vocabulary, also called
Lexeme , contains the context-independent information about a word. For example, no matter if "love" is used as a verb or a noun in some
context, its spelling and whether it consists of alphabetic characters
won't ever change. Its hash value will also always be the same.
for word in doc: lexeme = doc.vocab[word.text] print(lexeme.text, lexeme.orth, lexeme.shape_, lexeme.prefix_, lexeme.suffix_, lexeme.is_alpha, lexeme.is_digit, lexeme.is_title, lexeme.lang_)
The mapping of words to hashes doesn't depend on any state. To make sure each value is unique, spaCy uses a hash function to calculate the hash based on the word string. This also means that the hash for "coffee" will always be the same, no matter which model you're using or how you've configured spaCy.
However, hashes cannot be reversed and there's no way to resolve
3197928453018144401 back to "coffee". All spaCy can do is look it up in the vocabulary. That's why you always need to make
sure all objects you create have access to the same vocabulary. If they
don't, spaCy might not be able to find the strings it needs.
from spacy.tokens import Doc from spacy.vocab import Vocab doc = nlp(u'I like coffee') # original Doc assert doc.vocab.strings[u'coffee'] == 3197928453018144401 # get hash assert doc.vocab.strings == u'coffee' # 👍 empty_doc = Doc(Vocab()) # new Doc with empty Vocab # doc.vocab.strings will raise an error :( empty_doc.vocab.strings.add(u'coffee') # add "coffee" and generate hash assert doc.vocab.strings == u'coffee' # 👍 new_doc = Doc(doc.vocab) # create new doc with first doc's vocab assert doc.vocab.strings == u'coffee' # 👍
If the vocabulary doesn't contain a string for
3197928453018144401, spaCy will raise an error. You can re-add "coffee" manually, but this only works if you actually know that the document contains that word. To prevent this problem, spaCy will also export the
Vocab when you save a
nlp object. This will give you the object and its encoded annotations, plus they "key" to decode it.
If you've been modifying the pipeline, vocabulary, vectors and entities,
or made updates to the model, you'll eventually want to save your progress – for example, everything that's in your
nlp object. This means you'll have to translate its contents and structure into a format that can be saved, like a file or a byte string.
This process is called serialization. spaCy comes with built-in serialization methods and supports the Pickle protocol.
All container classes, i.e.
StringStore have the following methods available:
For example, if you've processed a very large document, you can use
Doc.to_disk to save it to a file on your local machine. This will save the document and its tokens, as well as the vocabulary associated with the
moby_dick = open('moby_dick.txt', 'r') # open a large document doc = nlp(moby_dick) # process it doc.to_disk('/moby_dick.bin') # save the processed Doc
If you need it again later, you can load it back into an empty
Doc with an empty
Vocab by calling
from spacy.tokens import Doc # to create empty Doc from spacy.vocab import Vocab # to create empty Vocab doc = Doc(Vocab()).from_disk('/moby_dick.bin') # load processed Doc
spaCy's models are statistical and every "decision" they make – for example, which part-of-speech tag to assign, or whether a word is a named entity – is a prediction. This prediction is based on the examples the model has seen during training. To train a model, you first need training data – examples of text, and the labels you want the model to predict. This could be a part-of-speech tag, a named entity or any other information.
The model is then shown the unlabelled text and will make a prediction. Because we know the correct answer, we can give the model feedback on its prediction in the form of an error gradient of the loss function that calculates the difference between the training example and the expected output. The greater the difference, the more significant the gradient and the updates to our model.
When training a model, we don't just want it to memorise our examples – we want it to come up with theory that can be generalised across other examples. After all, we don't just want the model to learn that this one instance of "Amazon" right here is a company – we want it to learn that "Amazon", in contexts like this, is most likely a company. That's why the training data should always be representative of the data we want to process. A model trained on Wikipedia, where sentences in the first person are extremely rare, will likely perform badly on Twitter. Similarly, a model trained on romantic novels will likely perform badly on legal text.
This also means that in order to know how the model is performing, and whether it's learning the right things, you don't only need training data – you'll also need evaluation data. If you only test the model with the data it was trained on, you'll have no idea how well it's generalising. If you want to train a model from scratch, you usually need at least a few hundred examples for both training and evaluation. To update an existing model, you can already achieve decent results with very few examples – as long as they're representative.
Every language is different – and usually full of exceptions and special cases, especially amongst the most common words. Some of these exceptions are shared across languages, while others are entirely specific – usually so specific that they need to be hard-coded. The lang module contains all language-specific data, organised in simple Python files. This makes the data easy to update and extend.
The shared language data in the directory root includes rules that can be generalised across languages – for example, rules for basic
punctuation, emoji, emoticons, single-letter abbreviations and norms for equivalent tokens with different spellings, like
”. This helps the models make more accurate predictions. The individual language data in a submodule contains rules that are only relevant to a particular language. It also takes care of putting together all components and creating the
Language subclass – for example,
| List of most common words of a language that are often useful to
filter out, for example "and" or "I". Matching tokens will return |
|Special-case rules for the tokenizer, for example, contractions like "can't" and abbreviations with punctuation, like "U.K.".|
|Norm exceptions norm_exceptions.py||Special-case rules for normalising tokens to improve the model's predictions, for example on American vs. British spelling.|
|Punctuation rules punctuation.py||Regular expressions for splitting tokens, e.g. on punctuation or special characters like emoji. Includes rules for prefixes, suffixes and infixes.|
|Character classes char_classes.py||Character classes to be used in regular expressions, for example, latin characters, quotes, hyphens or icons.|
|Lexical attributes lex_attrs.py|| Custom functions for setting lexical attributes on tokens, e.g. |
|Syntax iterators syntax_iterators.py|| Functions that compute views of a |
|Lemmatizer lemmatizer.py||Lemmatization rules or a lookup-based lemmatization table to assign base forms, for example "be" for "was".|
|Dictionary mapping strings in your tag set to Universal Dependencies tags.|
|Morph rules morph_rules.py||Exception rules for morphological analysis of irregular words like personal pronouns.|
The central data structures in spaCy are the
Doc and the
Doc object owns the sequence of tokens and all their annotations. The
Vocab object owns a set of look-up tables that make common information available across documents. By centralising strings, word
vectors and lexical attributes, we avoid storing multiple copies of this
data. This saves memory, and ensures there's a single source of truth.
Text annotations are also designed to allow a single source of truth: the
Doc object owns the data, and
Token are views that point into it. The
Doc object is constructed by the
Tokenizer, and then modified in place by the components of the pipeline. The
Language object coordinates these components. It takes raw text and sends it through the pipeline, returning an annotated document. It also orchestrates training and serialization.
| A text-processing pipeline. Usually you'll load this once per process as |
|A container for accessing linguistic annotations.|
|A slice from a |
|An individual token — i.e. a word, punctuation symbol, whitespace, etc.|
|An entry in the vocabulary. It's a word type with no context, as opposed to a word token. It therefore has no part-of-speech tag, dependency parse etc.|
| A lookup table for the vocabulary that allows you to access |
|Assign linguistic features like lemmas, noun case, verb tense etc. based on the word and its part-of-speech tag.|
|Map strings to and from hash values.|
| Segment text, and create |
|Determine the base forms of words.|
|Match sequences of tokens, based on pattern rules, similar to regular expressions.|
|Annotate part-of-speech tags on |
|Annotate syntactic dependencies on |
| Annotate named entities, e.g. persons or products, on |
|Container class for vector data keyed by string.|
|Container class for serializing collections of |
|Collection for training annotations.|
|An annotated corpus, using the JSON file format. Manages annotations for tagging, dependency parsing and NER.|
Community & FAQ
We're very happy to see the spaCy community grow and include a mix of people from all kinds of different backgrounds – computational linguistics, data science, deep learning, research and more. If you'd like to get involved, below are some answers to the most important questions and resources for further reading.
Help, my code isn't working!
Bugs suck, and we're doing our best to continuously improve the tests and fix bugs as soon as possible. Before you submit an issue, do a quick search and check if the problem has already been reported. If you're having installation or loading problems, make sure to also check out the troubleshooting guide. Help with spaCy is available via the following platforms:
|StackOverflow||Usage questions and everything related to problems with your specific code. The StackOverflow community is much larger than ours, so if your problem can be solved by others, you'll receive help much quicker.|
|Gitter chat||General discussion about spaCy, meeting other community members and exchanging tips, tricks and best practices. If we're working on experimental models and features, we usually share them on Gitter first.|
|GitHub issue tracker||Bug reports and improvement suggestions, i.e. everything that's likely spaCy's fault. This also includes problems with the models beyond statistical imprecisions, like patterns that point to a bug.|
How can I contribute to spaCy?
You don't have to be an NLP expert or Python pro to contribute, and we're
happy to help you get started. If you're new to spaCy, a good place to
start is the
help wanted (easy) label on GitHub, which we use to tag bugs and feature requests that are easy
and self-contained. We also appreciate contributions to the docs – whether
it's fixing a typo, improving an example or adding additional explanations.
You'll find a "Suggest edits" link at the bottom of each page that points
you to the source.
Another way of getting involved is to help us improve the language data – especially if you happen to speak one of the languages currently in alpha support. Even adding simple tokenizer exceptions, stop words or lemmatizer data can make a big difference. It will also make it easier for us to provide a statistical model for the language in the future. Submitting a test that documents a bug or performance issue, or covers functionality that's especially important for your application is also very helpful. This way, you'll also make sure we never accidentally introduce regressions to the parts of the library that you care about the most.
For more details on the types of contributions we're looking for, the code conventions and other useful tips, make sure to check out the contributing guidelines.
I've built something cool with spaCy – how can I get the word out?
First, congrats – we'd love to check it out! When you share your project on Twitter, don't forget to tag @spacy_io so we don't miss it. If you think your project would be a good fit for the showcase, feel free to submit it! Tutorials are also incredibly valuable to other users and a great way to get exposure. So we strongly encourage writing up your experiences, or sharing your code and some tips and tricks on your blog. Since our website is open-source, you can add your project or tutorial by making a pull request on GitHub.
If you would like to use the spaCy logo on your site, please get in touch and ask us first. However, if you want to show support and tell others that your project is using spaCy, you can grab one of our spaCy badges here:
<a href="https://alpha.spacy.io"><img src="https://img.shields.io/badge/built%20with-spaCy-09a3d5.svg" height="20"></a>
<a href="https://alpha.spacy.io"><img src="https://img.shields.io/badge/made%20with%20❤%20and-spaCy-09a3d5.svg" height="20"></a>
<a href="https://alpha.spacy.io"><img src="https://img.shields.io/badge/spaCy-v2-09a3d5.svg" height="20"></a>