Datasets:
Tasks:
Text Retrieval
Sub-tasks:
semantic-similarity-scoring
Languages:
German
Multilinguality:
monolingual
Size Categories:
n>1M
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 10,595 Bytes
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import random
from pathlib import Path
import datasets
from datasets import Value, Sequence, ClassLabel, Features
_CITATION = """\
coming soon
"""
_DESCRIPTION = """\
German Legal Sentences (GLS) is an automatically generated training dataset for semantic sentence
matching in the domain in german legal documents. It follows the concept of weak supervision, where
imperfect labels are generated using multiple heuristics. For this purpose we use a combination of
legal citation matching and BM25 similarity. The contained sentences and their citations are parsed
from real judicial decisions provided by [Open Legal Data](http://openlegaldata.io/)
"""
_VERSION = "0.0.2"
_DATA_URL = f"http://lavis.cs.hs-rm.de/storage/german-legal-sentences/GermanLegalSentences_v{_VERSION}.zip"
class GLSConfig(datasets.BuilderConfig):
"""BuilderConfig."""
def __init__(
self,
load_collection,
load_es_neighbors=None,
n_es_neighbors=None,
**kwargs,
):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(GLSConfig, self).__init__(**kwargs)
self.load_collection = load_collection
self.load_es_neighbors = load_es_neighbors
self.n_es_neighbors = n_es_neighbors
class GermanLegalSentences(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
GLSConfig(
name="sentences",
load_es_neighbors=False,
load_collection=False,
version=datasets.Version(_VERSION, ""),
description="Just the sentences and their masked references",
),
GLSConfig(
name="pairs",
load_es_neighbors=False,
load_collection=True,
version=datasets.Version(_VERSION, ""),
description="Sentence pairs sharing references",
),
GLSConfig(
name="pairs+es",
load_es_neighbors=True,
load_collection=True,
n_es_neighbors=5,
version=datasets.Version(_VERSION, ""),
description="Sentence pairs sharing references plus ES neighbors",
),
]
def _features(self):
if self.config.name == "sentences":
return datasets.Features(
{
"sent_id": Value("uint32"),
"doc_id": Value("uint32"),
"text": Value("string"),
"references": Sequence(
{
"ref_id": Value("uint32"),
"name": Value("string"),
"type": ClassLabel(names=["AZ", "LAW"]),
}
),
}
)
elif self.config.name == "pairs":
return Features(
{
"query.sent_id": Value("uint32"),
"query.doc_id": Value("uint32"),
"query.text": Value("string"),
"query.ref_ids": Sequence(Value("uint32")),
"related.sent_id": Value("uint32"),
"related.doc_id": Value("uint32"),
"related.text": Value("string"),
"related.ref_ids": Sequence(Value("uint32")),
}
)
elif self.config.name == "pairs+es":
return Features(
{
"query.sent_id": Value("uint32"),
"query.doc_id": Value("uint32"),
"query.text": Value("string"),
"query.ref_ids": Sequence(Value("uint32")),
"related.sent_id": Value("uint32"),
"related.doc_id": Value("uint32"),
"related.text": Value("string"),
"related.ref_ids": Sequence(Value("uint32")),
"es_neighbors.text": Sequence(Value("string")),
"es_neighbors.sent_id": Sequence(Value("uint32")),
"es_neighbors.doc_id": Sequence(Value("uint32")),
"es_neighbors.ref_ids": Sequence(
Sequence(datasets.Value("uint32"))
),
}
)
assert True
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=self._features(),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if dl_manager.manual_dir:
data_dir = Path(dl_manager.manual_dir)
else:
data_dir = Path(dl_manager.download_and_extract(_DATA_URL))
collection = _load_collection(data_dir) if self.config.load_collection else None
sent_ref_map = _load_sent_references(data_dir)
references = (
_load_reference_info(data_dir) if self.config.name == "sentences" else None
)
es_neighbors = (
_load_es_neighbors(data_dir) if self.config.load_es_neighbors else None
)
gen_kwargs = dict()
for split in ("train", "valid", "test"):
gen_kwargs[split] = {
"collection": collection,
"pair_id_file": data_dir / f"{split}.pairs.tsv",
"sentence_file": data_dir / f"{split}.sentences.tsv",
"references": references,
"sent_ref_map": sent_ref_map,
"es_neighbors": es_neighbors,
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs=gen_kwargs["train"]
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs=gen_kwargs["valid"]
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs=gen_kwargs["test"]
),
]
def _generate_examples(self, **kwargs):
if self.config.name.startswith("pairs"):
yield from self._generate_pairs(**kwargs)
elif self.config.name == "sentences":
yield from self._generate_sentences(**kwargs)
else:
assert True
def _generate_pairs(
self, pair_id_file, collection, sent_ref_map, es_neighbors, **kwargs
):
random.seed(17)
with open(pair_id_file, encoding="utf-8") as r:
idx = 0
for line in r:
stripped = line.rstrip()
if stripped:
a, b = stripped.split("\t")
features = {
"query.sent_id": int(a),
"query.doc_id": int(collection[a]["doc_id"]),
"query.text": collection[a]["text"],
"query.ref_ids": sent_ref_map[a],
"related.sent_id": int(b),
"related.doc_id": int(collection[b]["doc_id"]),
"related.text": collection[b]["text"],
"related.ref_ids": sent_ref_map[b],
}
if self.config.name == "pairs+es":
curr_es_neighbors = es_neighbors.get(a) or []
if len(curr_es_neighbors) < self.config.n_es_neighbors:
continue
es_sent_ids = random.sample(
curr_es_neighbors, k=self.config.n_es_neighbors
)
additional_features = {
"es_neighbors.sent_id": [int(i) for i in es_sent_ids],
"es_neighbors.doc_id": [
int(collection[i]["doc_id"]) for i in es_sent_ids
],
"es_neighbors.text": [
collection[i]["text"] for i in es_sent_ids
],
"es_neighbors.ref_ids": [
sent_ref_map[i] for i in es_sent_ids
],
}
features.update(additional_features)
yield idx, features
idx += 1
def _generate_sentences(
self,
sentence_file,
references,
sent_ref_map,
**kwargs,
):
with open(sentence_file, encoding="utf-8") as r:
for idx, line in enumerate(r):
stripped = line.rstrip()
if stripped == "":
continue
s_id, doc_id, text = stripped.split("\t", maxsplit=2)
yield idx, {
"sent_id": int(s_id),
"doc_id": int(doc_id),
"text": text,
"references": [
{
"ref_id": int(r_id),
"name": references[r_id][1],
"type": references[r_id][0],
}
for r_id in sent_ref_map[s_id]
],
}
def _load_collection(data_dir):
collection = dict()
for split in ("train", "valid", "test"):
with open(data_dir / f"{split}.sentences.tsv", encoding="utf-8") as r:
for line in r:
s_id, d_id, sent = line.strip().split("\t", maxsplit=2)
collection[s_id] = {"doc_id": d_id, "text": sent}
return collection
def _load_reference_info(data_dir):
with open(data_dir / "refs.tsv", encoding="utf-8") as r:
references = {
r_id: (r_type, r_name.rstrip())
for r_id, r_type, r_name in (
line.split("\t", maxsplit=2) for line in r if len(line) > 2
)
}
return references
def _load_sent_references(data_dir):
with open(data_dir / "sent_ref_map.tsv", encoding="utf-8") as r:
sent_ref_map = {
s_id: r_ids.rstrip().split()
for s_id, r_ids in (
line.split("\t", maxsplit=1) for line in r if len(line) > 2
)
}
return sent_ref_map
def _load_es_neighbors(data_dir):
with open(data_dir / "es_neighbors.tsv", encoding="utf-8") as r:
es_neighbors = {
s_id: other_s_ids.rstrip().split()
for s_id, other_s_ids in (
line.split("\t", maxsplit=1) for line in r if len(line) > 2
)
}
return es_neighbors
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