File size: 3,052 Bytes
d819851 | 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 | #!/usr/bin/env python3
"""
Kabyle-English Road Traffic Code Dataset - CSV Loader
Loads from dataset.csv and converts to HuggingFace format
"""
import pandas as pd
from datasets import load_dataset, Dataset, DatasetDict
def load_from_csv(csv_path="dataset.csv"):
"""Load dataset from CSV and convert to HuggingFace format"""
df = pd.read_csv(csv_path)
# Convert to list of dicts with proper structure
records = []
for _, row in df.iterrows():
record = {
"id": row["id"],
"translation": {
"en": row["english"],
"kab": row["kabyle"]
},
"category": row["category"],
"subcategory": row["subcategory"],
"domain": "road_traffic",
"complexity": row["complexity"],
"tokens_en": int(row["tokens_en"]),
"tokens_kab": int(row["tokens_kab"])
}
records.append(record)
return records
def create_splits(csv_path="dataset.csv"):
"""Create train/validation/test splits from CSV"""
records = load_from_csv(csv_path)
# Split based on ID suffix (matching original stratified split)
train_ids, val_ids, test_ids = [], [], []
for r in records:
idx = int(r["id"].split("_")[-1])
cat = r["category"]
# Reproduce stratified split logic
if cat == "dangers":
if idx % 10 < 7:
train_ids.append(r)
elif idx % 10 < 8:
val_ids.append(r)
else:
test_ids.append(r)
elif cat == "prohibitions":
if idx % 10 < 6:
train_ids.append(r)
elif idx % 10 < 8:
val_ids.append(r)
else:
test_ids.append(r)
elif cat == "obligations":
if idx % 10 < 5:
train_ids.append(r)
elif idx % 10 < 7:
val_ids.append(r)
else:
test_ids.append(r)
else: # end_of_restrictions
if idx % 10 < 4:
train_ids.append(r)
elif idx % 10 < 7:
val_ids.append(r)
else:
test_ids.append(r)
return {
"train": Dataset.from_list(train_ids),
"validation": Dataset.from_list(val_ids),
"test": Dataset.from_list(test_ids)
}
def load_dataset_from_csv(csv_path="dataset.csv"):
"""Main function - returns DatasetDict"""
splits = create_splits(csv_path)
return DatasetDict(splits)
if __name__ == "__main__":
print("Chargement depuis dataset.csv...")
dataset = load_dataset_from_csv()
print(f"\nTrain: {len(dataset['train'])} examples")
print(f"Validation: {len(dataset['validation'])} examples")
print(f"Test: {len(dataset['test'])} examples")
print("\n--- Exemple ---")
ex = dataset['train'][0]
print(f"ID: {ex['id']}")
print(f"EN: {ex['translation']['en']}")
print(f"KAB: {ex['translation']['kab']}")
print(f"Category: {ex['category']}")
|