spaCy/website/docs/api/goldparse.md

title	teaser	tag	source
GoldParse	A collection for training annotations	class	spacy/gold.pyx

GoldParse.__init__

Create a GoldParse. Unlike annotations in entities, label annotations in cats can overlap, i.e. a single word can be covered by multiple labelled spans. The TextCategorizer component expects true examples of a label to have the value 1.0, and negative examples of a label to have the value 0.0. Labels not in the dictionary are treated as missing – the gradient for those labels will be zero.

Name	Type	Description
doc	Doc	The document the annotations refer to.
words	iterable	A sequence of unicode word strings.
tags	iterable	A sequence of strings, representing tag annotations.
heads	iterable	A sequence of integers, representing syntactic head offsets.
deps	iterable	A sequence of strings, representing the syntactic relation types.
entities	iterable	A sequence of named entity annotations, either as BILUO tag strings, or as (start_char, end_char, label) tuples, representing the entity positions. If BILUO tag strings, you can specify missing values by setting the tag to None.
cats	dict	Labels for text classification. Each key in the dictionary may be a string or an int, or a (start_char, end_char, label) tuple, indicating that the label is applied to only part of the document (usually a sentence).
RETURNS	GoldParse	The newly constructed object.

GoldParse.__len__

Get the number of gold-standard tokens.

Name	Type	Description
RETURNS	int	The number of gold-standard tokens.

GoldParse.is_projective

Whether the provided syntactic annotations form a projective dependency tree.

Name	Type	Description
RETURNS	bool	Whether annotations form projective tree.

Attributes

Name	Type	Description
tags	list	The part-of-speech tag annotations.
heads	list	The syntactic head annotations.
labels	list	The syntactic relation-type annotations.
ents	list	The named entity annotations.
cand_to_gold	list	The alignment from candidate tokenization to gold tokenization.
gold_to_cand	list	The alignment from gold tokenization to candidate tokenization.
cats 2	list	Entries in the list should be either a label, or a (start, end, label) triple. The tuple form is used for categories applied to spans of the document.

Utilities

gold.docs_to_json

Convert a list of Doc objects into the JSON-serializable format used by the spacy train command.

Example

from spacy.gold import docs_to_json

doc = nlp(u"I like London")
json_data = docs_to_json([doc])

Name	Type	Description
docs	iterable / Doc	The Doc object(s) to convert.
id	int	ID to assign to the JSON. Defaults to 0.
RETURNS	list	The data in spaCy's JSON format.

gold.align

Calculate alignment tables between two tokenizations, using the Levenshtein algorithm. The alignment is case-insensitive.

The current implementation of the alignment algorithm assumes that both tokenizations add up to the same string. For example, you'll be able to align ["I", "'", "m"] and ["I", "'m"], which both add up to "I'm", but not ["I", "'m"] and ["I", "am"].

Example

from spacy.gold import align

bert_tokens = ["obama", "'", "s", "podcast"]
spacy_tokens = ["obama", "'s", "podcast"]
alignment = align(bert_tokens, spacy_tokens)
cost, a2b, b2a, a2b_multi, b2a_multi = alignment

Name	Type	Description
tokens_a	list	String values of candidate tokens to align.
tokens_b	list	String values of reference tokens to align.
RETURNS	tuple	A (cost, a2b, b2a, a2b_multi, b2a_multi) tuple describing the alignment.

The returned tuple contains the following alignment information:

Example

a2b = array([0, -1, -1, 2])
b2a = array([0, 2, 3])
a2b_multi = {1: 1, 2: 1}
b2a_multi = {}

If a2b[3] == 2, that means that tokens_a[3] aligns to tokens_b[2]. If there's no one-to-one alignment for a token, it has the value -1.

Name	Type	Description
cost	int	The number of misaligned tokens.
a2b	numpy.ndarray[ndim=1, dtype='int32']	One-to-one mappings of indices in tokens_a to indices in tokens_b.
b2a	numpy.ndarray[ndim=1, dtype='int32']	One-to-one mappings of indices in tokens_b to indices in tokens_a.
a2b_multi	dict	A dictionary mapping indices in tokens_a to indices in tokens_b, where multiple tokens of tokens_a align to the same token of tokens_b.
b2a_multi	dict	A dictionary mapping indices in tokens_b to indices in tokens_a, where multiple tokens of tokens_b align to the same token of tokens_a.

gold.biluo_tags_from_offsets

Encode labelled spans into per-token tags, using the BILUO scheme (Begin, In, Last, Unit, Out). Returns a list of unicode strings, describing the tags. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U". The string "-" is used where the entity offsets don't align with the tokenization in the Doc object. The training algorithm will view these as missing values. O denotes a non-entity token. B denotes the beginning of a multi-token entity, I the inside of an entity of three or more tokens, and L the end of an entity of two or more tokens. U denotes a single-token entity.

Example

from spacy.gold import biluo_tags_from_offsets

doc = nlp(u"I like London.")
entities = [(7, 13, "LOC")]
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ["O", "O", "U-LOC", "O"]

Name	Type	Description
doc	Doc	The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document.
entities	iterable	A sequence of (start, end, label) triples. start and end should be character-offset integers denoting the slice into the original string.
RETURNS	list	Unicode strings, describing the BILUO tags.

gold.offsets_from_biluo_tags

Encode per-token tags following the BILUO scheme into entity offsets.

Example

from spacy.gold import offsets_from_biluo_tags

doc = nlp(u"I like London.")
tags = ["O", "O", "U-LOC", "O"]
entities = offsets_from_biluo_tags(doc, tags)
assert entities == [(7, 13, "LOC")]

Name	Type	Description
doc	Doc	The document that the BILUO tags refer to.
entities	iterable	A sequence of BILUO tags with each tag describing one token. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U".
RETURNS	list	A sequence of (start, end, label) triples. start and end will be character-offset integers denoting the slice into the original string.

gold.spans_from_biluo_tags

Encode per-token tags following the BILUO scheme into Span objects. This can be used to create entity spans from token-based tags, e.g. to overwrite the doc.ents.

Example

from spacy.gold import offsets_from_biluo_tags

doc = nlp(u"I like London.")
tags = ["O", "O", "U-LOC", "O"]
doc.ents = spans_from_biluo_tags(doc, tags)

Name	Type	Description
doc	Doc	The document that the BILUO tags refer to.
entities	iterable	A sequence of BILUO tags with each tag describing one token. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U".
RETURNS	list	A sequence of Span objects with added entity labels.