Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Greek
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
other
Source Datasets:
original
License:
File size: 3,976 Bytes
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import os
from glob import glob
from pathlib import Path
from typing import List
import pandas as pd
from spacy.lang.el import Greek
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
base_path = Path("DATASETS/ENTITY RECOGNITION")
tokenizer = Greek().tokenizer
# A and D are different government gazettes
# A is the general one, publishing standard legislation, and D is meant for legislation on urban planning and such things
def process_document(ann_file: str, text_file: Path, metadata: dict, tokenizer) -> List[dict]:
"""Processes one document (.ann file and .txt file) and returns a list of annotated sentences"""
# read the ann file into a df
ann_df = pd.read_csv(ann_file, sep="\t", header=None, names=["id", "entity_with_span", "entity_text"])
sentences = [sent for sent in text_file.read_text().split("\n") if sent] # remove empty sentences
# split into individual columns
ann_df[["entity", "start", "end"]] = ann_df["entity_with_span"].str.split(" ", expand=True)
ann_df.start = ann_df.start.astype(int)
ann_df.end = ann_df.end.astype(int)
not_found_entities = 0
annotated_sentences = []
current_start_index = 0
for sentence in sentences:
ann_sent = {**metadata}
doc = tokenizer(sentence)
doc_start_index = current_start_index
doc_end_index = current_start_index + len(sentence)
current_start_index = doc_end_index + 1
relevant_annotations = ann_df[(ann_df.start >= doc_start_index) & (ann_df.end <= doc_end_index)]
for _, row in relevant_annotations.iterrows():
sent_start_index = row["start"] - doc_start_index
sent_end_index = row["end"] - doc_start_index
char_span = doc.char_span(sent_start_index, sent_end_index, label=row["entity"], alignment_mode="expand")
# ent_span = Span(doc, char_span.start, char_span.end, row["entity"])
if char_span:
doc.set_ents([char_span])
else:
not_found_entities += 1
print(f"Could not find entity `{row['entity_text']}` in sentence `{sentence}`")
ann_sent["words"] = [str(tok) for tok in doc]
ann_sent["ner"] = [tok.ent_iob_ + "-" + tok.ent_type_ if tok.ent_type_ else "O" for tok in doc]
annotated_sentences.append(ann_sent)
print(f"Did not find entities in {not_found_entities} cases")
return annotated_sentences
def read_to_df(split):
"""Reads the different documents and saves metadata"""
ann_files = glob(str(base_path / split / "ANN" / "*/*/*.ann"))
sentences = []
for ann_file in ann_files:
path = Path(ann_file)
year = path.parent.stem
file_name = path.stem
_, gazette, gazette_number, _, date = tuple(file_name.split(' '))
text_file = base_path / split / "TXT" / f"{gazette}/{year}/{file_name}.txt"
metadata = {
"date": date,
"gazette": gazette,
# "gazette_number": gazette_number,
}
sentences.extend(process_document(ann_file, text_file, metadata, tokenizer))
return pd.DataFrame(sentences)
splits = ["TRAIN", "VALIDATION", "TEST"]
train = read_to_df("TRAIN")
validation = read_to_df("VALIDATION")
test = read_to_df("TEST")
df = pd.concat([train, validation, test])
print(f"The final tagset (in IOB notation) is the following: `{list(df.ner.explode().unique())}`")
# save splits
def save_splits_to_jsonl(config_name):
# save to jsonl files for huggingface
if config_name: os.makedirs(config_name, exist_ok=True)
train.to_json(os.path.join(config_name, "train.jsonl"), lines=True, orient="records", force_ascii=False)
validation.to_json(os.path.join(config_name, "validation.jsonl"), lines=True, orient="records", force_ascii=False)
test.to_json(os.path.join(config_name, "test.jsonl"), lines=True, orient="records", force_ascii=False)
save_splits_to_jsonl("")
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