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 classmethodv2.0

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

Doc.get_extension classmethodv2.0

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 classmethodv2.0

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

Doc.remove_extension classmethodv2.0.12

Remove a previously registered extension.

Doc.char_span methodv2.0

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 mode `“strict”.

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.to_json methodv2.1

Convert a Doc to JSON. The format it produces will be the new format for the spacy train command (not implemented yet). If custom underscore attributes are specified, their values need to be JSON-serializable. They’ll be added to an "_" key in the data, e.g. "_": {"foo": "bar"}.

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.to_disk methodv2.0

Save the current state to a directory.

Doc.from_disk methodv2.0

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.retokenize contextmanagerv2.1

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 as a dictionary mapping attribute names to values as the "_" key.

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.merge method

Retokenize the document, such that the span at doc.text[start_idx : end_idx] is merged into a single token. If start_idx and end_idx do not mark start and end token boundaries, the document remains unchanged.

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.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.

Doc.sents propertyNeeds model

Iterate over the sentences in the document. Sentence spans have no label. To improve accuracy on informal texts, spaCy calculates sentence boundaries from the syntactic dependency parse. If the parser is disabled, the sents iterator will be unavailable.

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.