Datasets:
Tasks:
Token Classification
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Arabic
Size:
10K - 100K
License:
| import json, re, pathlib, unicodedata, itertools | |
| # helper regexes | |
| AR = re.compile(r'[\u0600-\u06FF]') # Arabic block | |
| EMOJI = re.compile('[' | |
| '\U0001F600-\U0001F64F' # emoticons | |
| '\U0001F300-\U0001F5FF' # symbols & pictographs | |
| '\U0001F680-\U0001F6FF' # transport & map symbols | |
| '\U0001F1E0-\U0001F1FF' # flags | |
| ']', flags=re.UNICODE) | |
| def is_flagged(txt): | |
| if EMOJI.search(txt) or any(c in '@#' for c in txt): | |
| return True | |
| non_ar = sum(1 for c in txt if not AR.match(c)) | |
| return len(txt) and non_ar / len(txt) >= 0.8 | |
| files = ['train.jsonl', 'validation.jsonl', 'test.jsonl'] | |
| tot = flagged = 0 | |
| examples = [] | |
| for fn in files: | |
| for row in map(json.loads, pathlib.Path(fn).read_text().splitlines()): | |
| for sp in row['spans']: | |
| txt = sp.get('text') or row['text'][sp['start']:sp['end']] | |
| tot += 1 | |
| if is_flagged(txt): | |
| flagged += 1 | |
| if len(examples) < 2000: | |
| examples.append(txt) | |
| print(f"{flagged}/{tot} spans flagged ({flagged/tot:.2%})") | |
| print("sample:", examples) | |