A Doc is a sequence of Token objects. Access sentences and named entities, export annotations to numpy arrays, losslessly serialize to compressed binary strings. The Doc object holds an array of TokenC structs. The Python-level Token and Span objects are views of this array, i.e. they don’t own the data themselves.

Doc.__init__ method

Construct a Doc object. The most common way to get a Doc object is via the nlp object.

Doc.__getitem__ method

Get a Token object at position i, where i is an integer. Negative indexing is supported, and follows the usual Python semantics, i.e. doc[-2] is doc[len(doc) - 2].

Get a Span object, starting at position start (token index) and ending at position end (token index). For instance, doc[2:5] produces a span consisting of tokens 2, 3 and 4. Stepped slices (e.g. doc[start : end : step]) are not supported, as Span objects must be contiguous (cannot have gaps). You can use negative indices and open-ended ranges, which have their normal Python semantics.

Doc.__iter__ method

Iterate over Token objects, from which the annotations can be easily accessed.

This is the main way of accessing Token objects, which are the main way annotations are accessed from Python. If faster-than-Python speeds are required, you can instead access the annotations as a numpy array, or access the underlying C data directly from Cython.

Doc.__len__ method

Get the number of tokens in the document.

Doc.set_extension classmethod

Define a custom attribute on the Doc which becomes available via Doc._. For details, see the documentation on custom attributes.

Doc.get_extension classmethod

Look up a previously registered extension by name. Returns a 4-tuple (default, method, getter, setter) if the extension is registered. Raises a KeyError otherwise.

Doc.has_extension classmethod

Check whether an extension has been registered on the Doc class.

Doc.remove_extension classmethod

Remove a previously registered extension.

Doc.char_span method

Create a Span object from the slice doc.text[start_idx:end_idx]. Returns None if the character indices don’t map to a valid span using the default alignment mode `“strict”.

Doc.set_ents methodv3.0

Set the named entities in the document.

Doc.similarity methodNeeds model

Make a semantic similarity estimate. The default estimate is cosine similarity using an average of word vectors.

Doc.count_by method

Count the frequencies of a given attribute. Produces a dict of {attr (int): count (ints)} frequencies, keyed by the values of the given attribute ID.

Doc.get_lca_matrix method

Calculates the lowest common ancestor matrix for a given Doc. Returns LCA matrix containing the integer index of the ancestor, or -1 if no common ancestor is found, e.g. if span excludes a necessary ancestor.

Doc.has_annotation method

Check whether the doc contains annotation on a Token attribute.

Doc.to_array method

Export given token attributes to a numpy ndarray. If attr_ids is a sequence of M attributes, the output array will be of shape (N, M), where N is the length of the Doc (in tokens). If attr_ids is a single attribute, the output shape will be (N,). You can specify attributes by integer ID (e.g. spacy.attrs.LEMMA) or string name (e.g. “LEMMA” or “lemma”). The values will be 64-bit integers.

Returns a 2D array with one row per token and one column per attribute (when attr_ids is a list), or as a 1D numpy array, with one item per attribute (when attr_ids is a single value).

Doc.from_array method

Load attributes from a numpy array. Write to a Doc object, from an (M, N) array of attributes.

Doc.from_docs staticmethodv3.0

Concatenate multiple Doc objects to form a new one. Raises an error if the Doc objects do not all share the same Vocab.

Doc.to_disk method

Save the current state to a directory.

Doc.from_disk method

Loads state from a directory. Modifies the object in place and returns it.

Doc.to_bytes method

Serialize, i.e. export the document contents to a binary string.

Doc.from_bytes method

Deserialize, i.e. import the document contents from a binary string.

Doc.to_json method

Serializes a document to JSON. Note that this is format differs from the deprecated JSON training format.

Doc.from_json methodv3.3.1

Deserializes a document from JSON, i.e. generates a document from the provided JSON data as generated by Doc.to_json().

Doc.retokenize contextmanager

Context manager to handle retokenization of the Doc. Modifications to the Doc’s tokenization are stored, and then made all at once when the context manager exits. This is much more efficient, and less error-prone. All views of the Doc (Span and Token) created before the retokenization are invalidated, although they may accidentally continue to work.

Retokenizer.merge method

Mark a span for merging. The attrs will be applied to the resulting token (if they’re context-dependent token attributes like LEMMA or DEP) or to the underlying lexeme (if they’re context-independent lexical attributes like LOWER or IS_STOP). Writable custom extension attributes can be provided using the "_" key and specifying a dictionary that maps attribute names to values.

Retokenizer.split method

Mark a token for splitting, into the specified orths. The heads are required to specify how the new subtokens should be integrated into the dependency tree. The list of per-token heads can either be a token in the original document, e.g. doc[2], or a tuple consisting of the token in the original document and its subtoken index. For example, (doc[3], 1) will attach the subtoken to the second subtoken of doc[3].

This mechanism allows attaching subtokens to other newly created subtokens, without having to keep track of the changing token indices. If the specified head token will be split within the retokenizer block and no subtoken index is specified, it will default to 0. Attributes to set on subtokens can be provided as a list of values. They’ll be applied to the resulting token (if they’re context-dependent token attributes like LEMMA or DEP) or to the underlying lexeme (if they’re context-independent lexical attributes like LOWER or IS_STOP).

Doc.ents propertyNeeds model

The named entities in the document. Returns a tuple of named entity Span objects, if the entity recognizer has been applied.

Doc.spans property

A dictionary of named span groups, to store and access additional span annotations. You can write to it by assigning a list of Span objects or a SpanGroup to a given key.

Doc.cats propertyNeeds model

Maps a label to a score for categories applied to the document. Typically set by the TextCategorizer.

Doc.noun_chunks propertyNeeds model

Iterate over the base noun phrases in the document. Yields base noun-phrase Span objects, if the document has been syntactically parsed. A base noun phrase, or “NP chunk”, is a noun phrase that does not permit other NPs to be nested within it – so no NP-level coordination, no prepositional phrases, and no relative clauses.

To customize the noun chunk iterator in a loaded pipeline, modify nlp.vocab.get_noun_chunks. If the noun_chunk syntax iterator has not been implemented for the given language, a NotImplementedError is raised.

Doc.sents propertyNeeds model

Iterate over the sentences in the document. Sentence spans have no label.

This property is only available when sentence boundaries have been set on the document by the parser, senter, sentencizer or some custom function. It will raise an error otherwise.

Doc.has_vector propertyNeeds model

A boolean value indicating whether a word vector is associated with the object.

Doc.vector propertyNeeds model

A real-valued meaning representation. Defaults to an average of the token vectors.

Doc.vector_norm propertyNeeds model

The L2 norm of the document’s vector representation.

Attributes

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.