changed notation scheme to IOB

README.md

@@ -3,7 +3,7 @@ annotations_creators:
 - other
 language_creators:
 - found
-languages:
+language:
 - el
 license:
 - cc-by-nc-sa-4.0
@@ -93,6 +93,8 @@ The files contain the following data fields
     - `PUBLIC-DOCS`: Public Document Reference; any reference to documents or decisions that have been published by a public institution (organization) that are not considered a primary source of legislation (e.g., local decisions, announcements, memorandums, directives).
     - `O`: No entity annotation present
 
+The final tagset (in IOB notation) is the following: `['O', 'B-ORG', 'I-ORG', 'B-GPE', 'I-GPE', 'B-LEG-REFS', 'I-LEG-REFS', 'B-PUBLIC-DOCS', 'I-PUBLIC-DOCS', 'B-PERSON', 'I-PERSON', 'B-FACILITY', 'I-FACILITY', 'B-LOCATION-UNK', 'I-LOCATION-UNK', 'B-LOCATION-NAT', 'I-LOCATION-NAT']`
+
 ### Data Splits
 
 The dataset has three splits: *train*, *validation* and *test*.

convert_to_hf_dataset.py

@@ -0,0 +1,101 @@
+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("")

test.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:76d25a3c1a64ad408e3272aee091f98eddf178b360aa550476c7e8b985f71771
-size 2263011
+oid sha256:5ad0654caac860a9d6d3d7d7834f913afaafc6cc5506c379c50c3d86ef12d559
+size 2278020

train.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f308076197d3ddfaa21ad7f99275cd4b545d68531698aed434f42cdcb31e8fec
-size 9153743
+oid sha256:1e0a17fd23cf770e960e14d4972c3245bc9a8f65cf616a8db4a7daaf4fd0e519
+size 9226475

validation.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:06d029554fed1f2b49d17ff12a1d0084fc18845d79737c28ece8f243a100e030
-size 2428314
+oid sha256:d91aab2c40560e498d8948fd4133eda83f519ebb2575d59901b343b8f91dda15
+size 2445108