dataset_creation_scripts/annotate.py

import json
import pickle
from pathlib import Path

from spacy.tokens import Span

import dacy
from dacy.datasets import dane


def load_examples():
    save_path = Path("examples.pkl")
    if save_path.exists():
        with open(save_path, "rb") as f:
            examples = pickle.load(f)

        return examples

    train, dev, test = dane()

    nlp = dacy.load("da_dacy_large_ner_fine_grained-0.1.0")

    examples = list(test(nlp)) + list(train(nlp)) + list(dev(nlp))

    docs = nlp.pipe([ex.x.text for ex in examples])

    for e in examples:
        e.predicted = next(docs)

    with open("examples.pkl", "wb") as f:
        pickle.dump(examples, f)

    return examples


def normalize_examples(examples):
    label_mapping = {
        "PER": "PERSON",
        "LOC": "LOCATION",
        "ORG": "ORGANIZATION",
        "MISC": "MISC",
    }

    for e in examples:
        old_ents = e.y.ents
        new_ents = []
        for ent in old_ents:
            new_label = label_mapping[ent.label_]
            new_ent = Span(e.y, start=ent.start, end=ent.end, label=new_label)
            new_ents.append(new_ent)

        e.y.ents = new_ents

    return examples


def example_to_review_format(example) -> dict:
    ref = example.y

    text = ref.text
    tokens = [
        {"text": t.text, "start": t.idx, "end": t.idx + len(t), "id": i}
        for i, t in enumerate(ref)
    ]
    answer = "accept"

    versions = []

    v_ref_spans = [
        {
            "start": s.start_char,
            "end": s.end_char,
            "label": s.label_,
            "token_start": s.start,
            "token_end": s.end - 1,
        }
        for s in ref.ents
    ]
    v_ref = {
        "text": text,
        "tokens": tokens,
        "spans": v_ref_spans,
        "answer": answer,
        "sessions": ["reference"],
        "default": True,
    }
    versions.append(v_ref)

    v_pred_spans = [
        {
            "start": s.start_char,
            "end": s.end_char,
            "label": s.label_,
            "token_start": s.start,
            "token_end": s.end - 1,
        }
        for s in example.predicted.ents
    ]
    v_pred = {
        "text": text,
        "tokens": tokens,
        "spans": v_pred_spans,
        "answer": answer,
        "sessions": ["da_dacy_large_ner_fine_grained-0.1.0"],
        "default": True,
    }
    versions.append(v_pred)

    return {
        "text": text,
        "tokens": tokens,
        "answer": answer,
        "view_id": "ner_manual",
        "versions": versions,
    }


if __name__ == "__main__":
    examples = load_examples()
    
    ",".join(set([ent.label_ for e in examples for ent in e.x.ents]))

    jsonl_data = [example_to_review_format(e) for e in normalize_examples(examples)]

    with open("examples.jsonl", "w") as f:
        for json_dict in jsonl_data:
            line = json.dumps(json_dict)
            f.write(f"{line}\n")

    with open("reference.jsonl", "w") as f:
        for json_dict in jsonl_data:
            line = json.dumps(json_dict["versions"][0])
            f.write(f"{line}\n")

    with open("predictions.jsonl", "w") as f:
        for json_dict in jsonl_data:
            line = json.dumps(json_dict["versions"][1])
            f.write(f"{line}\n")