File size: 4,023 Bytes
ede3d9b b30dfd8 78295a5 187035b b30dfd8 78295a5 187035b ede3d9b fcd3ae9 a22e20f e55621c 2750d92 e55621c bdfbf26 2750d92 bdfbf26 d182e55 fcd3ae9 d182e55 | 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 | 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",
}
TASK_NAMES = [
"ATS-Jurisdiction", "ATS-FavorableJudgment", "Chevron-Agency", "Chevron-ChevCited",
"Chevron-Dec.Ov.", "Chevron-Deference", "Chevron-Outcome", "Chevron-Subject",
"CoA-casetyp1", "CoA-direct1", "CoA-geniss", "CoA-typeiss",
"DC-casetype", "DC-category", "DC-libcon",
"JRC-AREA1", "JRC-CERT", "JRC-REVERSD",
"SC-decisionDirection", "SC-issueArea", "SC-partyWinning",
"SC-petitioner", "SC-precedentAlteration",
"SSC-ca_disp", "SSC-ca_uscty", "SSC-death_c", "SSC-p1_persn"
]
_CONFIGS = {
task_name: {
"description": f"{task_name} specific legal opinions", # Dynamic description based on task name
"features": {
"Citation": datasets.Value("string"),
"Full 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"),
},
}
for task_name in TASK_NAMES
}
class LongConLDataset(datasets.GeneratorBasedBuilder):
"""Legal opinion classification dataset for LongConL tasks"""
def _info(self):
"""Return dataset information."""
features = datasets.Features({
"Citation": datasets.Value("string"),
"Full 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")
print(f"Number of examples to generate: {len(data_dict)}")
for id_, row in enumerate(data_dict):
yield id_, {
"Citation": row["Citation"],
"Full Case Name": row["Full 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 TASK_NAMES
]
|