CODEWITHSUNDEEP
pythonspacy
senty
senty

Reputation: 12847

Converting Spacy Training Data format to Spacy CLI Format (for blank NER)

This is the classic training format.

TRAIN_DATA = [
    ("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
    ("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
]

I used to train with code but as I understand, the training is better with CLI train method. However, my format is this.

I have found code-snippets for this type of conversion but every one of them is performing spacy.load('en') rather than going with blank - which made me think, are they training existing model rather than blank?

This chunk seems pretty easy:

import spacy
from spacy.gold import docs_to_json
import srsly

nlp = spacy.load('en', disable=["ner"]) # as you see it's loading 'en' which I don't have
TRAIN_DATA = #data from above

docs = []
for text, annot in TRAIN_DATA:
    doc = nlp(text)
    doc.ents = [doc.char_span(start_idx, end_idx, label=label) for start_idx, end_idx, label in annot["entities"]]
    docs.append(doc)

srsly.write_json("ent_train_data.json", [docs_to_json(docs)])

Running this code throws me: Can't find model 'en'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory.

I am quite confused how to use it with spacy train on blank. Just use spacy.blank('en')? But then what about disable=["ner"] flag?

Edit:

If I try spacy.blank('en') instead, i receive Can't import language goal from spacy.lang: No module named 'spacy.lang.en'

Edit 2: I have tried loading en_core_web_sm

nlp = spacy.load('en_core_web_sm')

docs = []
for text, annot in TRAIN_DATA:
    doc = nlp(text)
    doc.ents = [doc.char_span(start_idx, end_idx, label=label) for start_idx, end_idx, label in annot["entities"]]
    docs.append(doc)

srsly.write_json("ent_train_data.json", [docs_to_json(docs)])

TypeError: object of type 'NoneType' has no len()

Ailton - print(text[start:end])

Goal! FK Qarabag 1, Partizani Tirana 0. Filip Ozobic - FK Qarabag - shot with the head from the centre of the box to the centre of the goal. Assist - Ailton - print(text)

None - doc.ents =... line

TypeError: object of type 'NoneType' has no len()

Edit 3: From Ines' comment

nlp = spacy.load('en_core_web_sm')

docs = []
for text, annot in TRAIN_DATA:

    doc = nlp(text)

    tags = biluo_tags_from_offsets(doc, annot['entities'])
    docs.append(doc)

srsly.write_json(train_name + "_spacy_format.json", [docs_to_json(docs)])

This created the json but I don't see any of my tagged entities in the generated json.

Upvotes: 4

Views: 5272

Answers (2)

Chad Dubiel
Chad Dubiel

Reputation: 51

import spacy
import srsly
from spacy.training import docs_to_json, offsets_to_biluo_tags, biluo_tags_to_spans

TRAIN_DATA = [
    ("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
    ("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
]

nlp = spacy.load('en_core_web_lg')
docs = []
for text, annot in training_sub:
    doc = nlp(text)
    tags = offsets_to_biluo_tags(doc, annot['entities'])
    entities = biluo_tags_to_spans(doc, tags)
    doc.ents = entities
    docs.append(doc)

srsly.write_json("spacy_format.json", [docs_to_json(docs)])

As of spaCy v3.1, the above code works. Some relevant methods from spacy.gold have been renamed and migrated to spacy.training.

Upvotes: 5

aab
aab

Reputation: 11474

Edit 3 is close, but you're missing a step where you add the entities to the document. This should work:

import spacy
import srsly
from spacy.gold import docs_to_json, biluo_tags_from_offsets, spans_from_biluo_tags

TRAIN_DATA = [
    ("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
    ("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
]

nlp = spacy.load('en_core_web_sm')
docs = []
for text, annot in TRAIN_DATA:
    doc = nlp(text)
    tags = biluo_tags_from_offsets(doc, annot['entities'])
    entities = spans_from_biluo_tags(doc, tags)
    doc.ents = entities
    docs.append(doc)

srsly.write_json("spacy_format.json", [docs_to_json(docs)])

It would be good to add a built-in function to do this conversion, since it's common to want to shift from the example scripts (which are just meant to be simple demos) to the train CLI.

Edit:

You can also skip the somewhat indirect use of the built-in BILUO converters and use what you had above:

    doc.ents = [doc.char_span(start_idx, end_idx, label=label) for start_idx, end_idx, label in annot["entities"]]

Upvotes: 7

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