Datasets:
Tasks:
Text Classification
Formats:
parquet
Languages:
Ancient Greek (to 1453)
Size:
100K - 1M
License:
File size: 7,317 Bytes
1f231e9 24c3b46 1f231e9 1bcc6c0 1f231e9 85f00da 1f231e9 4a0d2b0 ecfe7f5 4a0d2b0 1f231e9 4a0d2b0 1f231e9 4a0d2b0 8373f72 4a0d2b0 8373f72 4a0d2b0 ecfe7f5 8373f72 4a0d2b0 85f00da ecfe7f5 85f00da ecfe7f5 85f00da ecfe7f5 4a0d2b0 24c3b46 1bcc6c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | from collections import Counter, defaultdict
import json
from pathlib import Path
import pyarrow.parquet as pq
from scripts.metrical_lines import (
PUBLIC_METRICAL_LINE_FIELD_SET,
load_public_metrical_lines,
sanitize_metrical_lines,
)
DATA_ROOT = Path(__file__).resolve().parents[1] / "data"
BASE_CONFIGS = ("prose", "verse_sentence", "verse_metre")
def test_atomic_splits_are_balanced_and_100_chunkable() -> None:
for base_config in BASE_CONFIGS:
config = f"{base_config}_1"
counts = defaultdict(Counter)
for path in sorted((DATA_ROOT / config).glob("*.parquet")):
table = pq.read_table(path, columns=["author", "split"])
for row in table.to_pylist():
counts[row["author"]][row["split"]] += 1
assert counts
for author, author_counts in counts.items():
assert author_counts["train"] > 0, (config, author)
assert author_counts["validation"] == author_counts["test"], (config, author)
assert author_counts["validation"] >= 100, (config, author)
assert author_counts["validation"] % 100 == 0, (config, author)
def test_each_genre_retains_approximately_balanced_source_splits() -> None:
for base_config in BASE_CONFIGS:
config = f"{base_config}_1"
counts = Counter()
for path in sorted((DATA_ROOT / config).glob("*.parquet")):
splits = pq.read_table(path, columns=["split"])["split"].to_pylist()
counts.update(splits)
total = sum(counts.values())
assert total > 0
assert counts["validation"] == counts["test"]
assert 0.78 <= counts["train"] / total <= 0.83, (config, counts)
def test_chunkable_variants_have_atomic_train_and_exact_evaluation_chunks() -> None:
for base_config in BASE_CONFIGS:
for threshold in (10, 100):
config = f"{base_config}_{threshold}"
train_path = DATA_ROOT / config / "train-00000-of-00001.parquet"
train_sizes = pq.read_table(train_path, columns=["chunk_size"])["chunk_size"].to_pylist()
assert train_sizes and set(train_sizes) == {1}
for split in ("validation", "test"):
path = DATA_ROOT / config / f"{split}-00000-of-00001.parquet"
table = pq.read_table(
path, columns=["author", "chunk_size", "constituent_ids"],
)
assert table.num_rows > 0
assert set(table["chunk_size"].to_pylist()) == {threshold}
assert all(
len(ids) == threshold for ids in table["constituent_ids"].to_pylist()
)
def test_all_task_sizes_cover_exactly_the_same_atomic_rows() -> None:
for base_config in BASE_CONFIGS:
atomic_rows = {}
for split in ("train", "validation", "test"):
path = DATA_ROOT / f"{base_config}_1" / f"{split}-00000-of-00001.parquet"
atomic_rows[split] = {
row["id"]: row["text"]
for row in pq.read_table(path, columns=["id", "text"]).to_pylist()
}
for threshold in (10, 100):
for split in ("train", "validation", "test"):
path = (
DATA_ROOT / f"{base_config}_{threshold}"
/ f"{split}-00000-of-00001.parquet"
)
chunks = pq.read_table(
path, columns=["text", "constituent_ids"],
).to_pylist()
represented_ids = [
row_id for chunk in chunks for row_id in chunk["constituent_ids"]
]
assert len(represented_ids) == len(set(represented_ids))
assert set(represented_ids) == set(atomic_rows[split]), (
base_config,
threshold,
split,
)
separator = "\n" if base_config == "verse_metre" else "\n\n"
for chunk in chunks:
expected_text = (
atomic_rows[split][chunk["constituent_ids"][0]]
if split == "train"
else separator.join(
atomic_rows[split][row_id].strip()
for row_id in chunk["constituent_ids"]
)
)
assert chunk["text"] == expected_text
def test_verse_metre_chunks_concatenate_every_constituent_syllable() -> None:
atomic_syllables = {}
for split in ("train", "validation", "test"):
path = DATA_ROOT / "verse_metre_1" / f"{split}-00000-of-00001.parquet"
table = pq.read_table(path, columns=["id", "syllables"])
atomic_syllables[split] = {
row["id"]: json.loads(row["syllables"])
for row in table.to_pylist()
}
for threshold in (10, 100):
for split in ("train", "validation", "test"):
path = (
DATA_ROOT / f"verse_metre_{threshold}"
/ f"{split}-00000-of-00001.parquet"
)
table = pq.read_table(path, columns=["constituent_ids", "syllables"])
for row in table.to_pylist():
expected = [
syllable
for row_id in row["constituent_ids"]
for syllable in atomic_syllables[split][row_id]
]
assert json.loads(row["syllables"]) == expected
def test_scansion_is_not_published() -> None:
for suffix in ("1", "10", "100"):
for split in ("train", "validation", "test"):
path = (
DATA_ROOT / f"verse_metre_{suffix}"
/ f"{split}-00000-of-00001.parquet"
)
assert "scansion" not in pq.ParquetFile(path).schema_arrow.names
def test_metrical_lines_publish_only_content_allowlist_everywhere() -> None:
for suffix in ("1", "10", "100"):
for split in ("train", "validation", "test"):
path = (
DATA_ROOT / f"verse_sentence_{suffix}"
/ f"{split}-00000-of-00001.parquet"
)
table = pq.read_table(
path, columns=["metrical_line_ids", "metrical_lines"],
)
for row in table.to_pylist():
lines = load_public_metrical_lines(row["metrical_lines"])
assert len(lines) == len(row["metrical_line_ids"])
assert all(set(line) == PUBLIC_METRICAL_LINE_FIELD_SET for line in lines)
def test_metrical_line_sanitizer_drops_all_provenance_and_identifiers() -> None:
unsafe = json.dumps([{
"text": "μῆνιν ἄειδε",
"metre": "hexameter",
"syllables": [{"text": "μῆ", "quantity": "long", "features": []}],
"hypotactic_file": "iliad1.html",
"hypotactic_author": "Homer",
"hypotactic_work": "Iliad",
"book": "1",
"poem_sequence": "1",
"number": "1",
"speaker": "Achilles",
"id": "vm-secret",
"coverage_in_sentence": "full",
"scansion": "–",
}], ensure_ascii=False)
lines = json.loads(sanitize_metrical_lines(unsafe))
assert len(lines) == 1
assert set(lines[0]) == PUBLIC_METRICAL_LINE_FIELD_SET
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