Datasets:

lavis-nlp
/

german_legal_sentences

	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