2017-10-24 13:50:41 +00:00
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//- 💫 DOCS > API > ANNOTATION > TRAINING
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p
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| spaCy takes training data in JSON format. The built-in
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| #[+api("cli#convert") #[code convert]] command helps you convert the
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| #[code .conllu] format used by the
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| #[+a("https://github.com/UniversalDependencies") Universal Dependencies corpora]
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| to spaCy's training format.
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+aside("Annotating entities")
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| Named entities are provided in the #[+a("/api/annotation#biluo") BILUO]
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| notation. Tokens outside an entity are set to #[code "O"] and tokens
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| that are part of an entity are set to the entity label, prefixed by the
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| BILUO marker. For example #[code "B-ORG"] describes the first token of
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| a multi-token #[code ORG] entity and #[code "U-PERSON"] a single
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2017-10-26 12:44:32 +00:00
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| token representing a #[code PERSON] entity. The
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| #[+api("goldparse#biluo_tags_from_offsets") #[code biluo_tags_from_offsets]]
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| function can help you convert entity offsets to the right format.
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2017-10-24 13:50:41 +00:00
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+code("Example structure").
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[{
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"id": int, # ID of the document within the corpus
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"paragraphs": [{ # list of paragraphs in the corpus
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"raw": string, # raw text of the paragraph
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"sentences": [{ # list of sentences in the paragraph
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"tokens": [{ # list of tokens in the sentence
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"id": int, # index of the token in the document
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"dep": string, # dependency label
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"head": int, # offset of token head relative to token index
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"tag": string, # part-of-speech tag
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"orth": string, # verbatim text of the token
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"ner": string # BILUO label, e.g. "O" or "B-ORG"
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}],
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"brackets": [{ # phrase structure (NOT USED by current models)
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"first": int, # index of first token
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"last": int, # index of last token
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"label": string # phrase label
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}]
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}]
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}]
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}]
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p
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| Here's an example of dependencies, part-of-speech tags and names
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| entities, taken from the English Wall Street Journal portion of the Penn
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| Treebank:
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+github("spacy", "examples/training/training-data.json", false, false, "json")
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