JSON generated by NER Annotator doesn't seem to work with Spacy convertor

[tecoholic]

See comment at #43 (reply in thread)

[MikhailKlemin]

Hello there!

A simple function to convert generated by ner-annotator JSON directly to docbin would be this one:

from spacy.tokens import DocBin
import spacy
import json
from tqdm import tqdm
import random

nlp = spacy.blank("en")


def load_data(file):
    with open(file, "r", encoding="utf-8") as f:
        data = json.load(f)
    return (data["annotations"])


train_data = load_data("./data/annotation_1.json")
valid_data = load_data("./data/annotation_3.json")


def create_training(TRAIN_DATA):
    db = DocBin()
    for text, annot in tqdm(TRAIN_DATA):
        doc = nlp.make_doc(text)
        ents = []
        for start, end, label in annot["entities"]:
            span = doc.char_span(start, end, label=label,
                                 alignment_mode="contract")
            if span is None:
                print("Skipping entity")
            else:
                ents.append(span)
        doc.ents = ents
        db.add(doc)
    return (db)


train_data = create_training(train_data)
train_data.to_disk("./data/train2.spacy")
valid_data = create_training(valid_data)
valid_data.to_disk("./data/valid2.spacy")

PS Good job for the app, I love it.

[tecoholic]

@MikhailKlemin Thank you for coming up with the solution.

[ankitladva11]

I am facing issues in saving it to disk .spacy file, what to do ?
Thanks in advance

[ankitladva11]

I am facing issues in saving it to disk .spacy file, what to do ? Thanks in advance

Resolved!!

[alvi-khan]

Hey @ankitladva11! Glad to know your problem was resolved. When you have the time, could you please leave a comment describing your issue and how you managed to resolve it? It would be useful to future users who might face the same issue. TIA!

[DaanDeSmedt]

@dreji18 also has a nicely documented approach to getting Spacy to work with the NER Annotator export.

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