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ede3d9b b30dfd8 78295a5 187035b b30dfd8 78295a5 187035b ede3d9b bdfbf26 17388f1 78295a5 17388f1 ede3d9b 78295a5 ede3d9b b30dfd8 78295a5 b30dfd8 78295a5 ede3d9b 78295a5 17388f1 187035b ede3d9b 78295a5 ede3d9b 187035b ede3d9b 78295a5 ede3d9b 187035b a926970 78295a5 a926970 ede3d9b 78295a5 8ef5ff3 a35ba9c 78295a5 8ef5ff3 78295a5 17388f1 b30dfd8 | 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 | import datasets
import pandas as pd
# Dataset metadata
_CITATION = """"""
_DESCRIPTION = """"""
_HOMEPAGE = ""
_LICENSE = ""
# Updated URLs to dynamically handle task names
_URLS = {
"train": "data/LongConL-tasks/{task_name}/train.csv",
"validation": "data/LongConL-tasks/{task_name}/validation.csv",
"test": "data/LongConL-tasks/{task_name}/test.csv",
}
_CONFIGS = {
"ATS-Jurisdiction": {
"description": "Jurisdiction-specific legal opinions",
"features": {
"Citation": datasets.Value("string"),
"Case Name": datasets.Value("string"),
"Opinion Text": datasets.Value("string"),
"Numerical Label": datasets.Value("string"), # Will be optional for some tasks
"Text Label": datasets.Value("string"),
},
},
"ATS-FavorableJudgment": {
"description": "Description for other task 1",
"features": {
"Citation": datasets.Value("string"),
"Case Name": datasets.Value("string"),
"Opinion Text": datasets.Value("string"),
"Numerical Label": datasets.Value("string"),
"Text Label": datasets.Value("string"),
},
}
}
class LongConLDataset(datasets.GeneratorBasedBuilder):
"""Legal opinion classification dataset for LongConL tasks"""
def _info(self):
"""Return dataset information."""
features = datasets.Features({
"Citation": datasets.Value("string"),
"Case Name": datasets.Value("string"),
"Opinion Text": datasets.Value("string"),
"Numerical Label": datasets.Value("string"), # Will be optional for some tasks
"Text Label": datasets.Value("string"),
})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Split the dataset into train, validation, and test."""
task_name = self.config.name # Get the current task name from the config
urls = {key: val.format(task_name=task_name) for key, val in _URLS.items()} # Update URLs with the task name
downloaded_files = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"file_path": downloaded_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"file_path": downloaded_files["validation"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"file_path": downloaded_files["test"]},
),
]
def _generate_examples(self, file_path):
"""Generate examples from the dataset CSV."""
data = pd.read_csv(file_path)
print("Data loaded from file:", file_path)
print(data.head()) # Display first few rows
data_dict = data.to_dict(orient="records")
for id_, row in enumerate(data_dict):
yield id_, {
"Citation": row["Citation"],
"Case Name": row["Case Name"],
"Opinion Text": row["Opinion Text"],
"Numerical Label": row.get("Numerical Label", None), # Use .get() to handle missing keys
"Text Label": row["Text Label"],
}
# Use a dynamic config
BUILDER_CONFIGS = [
datasets.BuilderConfig(name=task_name, version=datasets.Version("1.0.0"), description=task_name)
for task_name in ["ATS-Jurisdiction", "Other-Task-Name-1", "Other-Task-Name-2"] # Add your task names here
]
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