Datasets:
wiki_dpr

	import os

	import numpy as np

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """
	@inproceedings{karpukhin-etal-2020-dense,
	title = "Dense Passage Retrieval for Open-Domain Question Answering",
	author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
	month = nov,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
	doi = "10.18653/v1/2020.emnlp-main.550",
	pages = "6769--6781",
	}
	"""

	_DESCRIPTION = """
	This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
	It contains 21M passages from wikipedia along with their DPR embeddings.
	The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.
	"""

	_LICENSE = """DPR is CC-BY-NC 4.0 licensed."""

	_DATA_URL = "https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz"

	_NQ_VECTORS_URL = "https://dl.fbaipublicfiles.com/dpr/data/wiki_encoded/single/nq/wiki_passages_{i}"

	_MULTISET_VECTORS_URL = "https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_{i}"

	_INDEX_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr"


	class WikiDprConfig(datasets.BuilderConfig):
	"""BuilderConfig for WikiDprConfig."""

	def __init__(
	self,
	with_embeddings=True,
	with_index=True,
	wiki_split="psgs_w100",
	embeddings_name="nq",
	index_name="compressed",
	index_train_size=262144,
	dummy=False,
	**kwargs,
	):
	"""BuilderConfig for WikiSnippets.
	Args:
	with_embeddings (`bool`, defaults to `True`): Load the 768-dimensional embeddings from DPR.
	with_index (`bool`, defaults to `True`): Load the faiss index trained on the embeddings.
	wiki_split (`str`, defaults to `psgs_w100`): name of the splitting method of wiki articles.
	embeddings_name (`str`, defaults to `nq`): "nq" or "multiset", depending on which dataset DPR was trained on.
	index_name (`str`, defaults to `compressed`): "compressed" or "exact", the configuration of the faiss index to use.
	index_train_size (`int`, defaults to `262144`): Size of the subset to use to train the index, if it is trainable.
	dummy (`bool`, defaults to `False`): Dummy uses only 10 000 examples for testing purposes.
	**kwargs: keyword arguments forwarded to super.
	"""
	self.with_embeddings = with_embeddings
	self.with_index = with_index and index_name != "no_index"
	self.wiki_split = wiki_split
	self.embeddings_name = embeddings_name
	self.index_name = index_name if with_index else "no_index"
	self.index_train_size = index_train_size
	self.dummy = dummy
	name = [self.wiki_split, self.embeddings_name, self.index_name]
	if not self.with_embeddings:
	name.append("no_embeddings")
	if self.dummy:
	name = ["dummy"] + name
	assert (
	self.index_name != "compressed" or not self.with_index
	), "Please use `index_name='exact' for dummy wiki_dpr`"
	assert wiki_split == "psgs_w100"
	assert embeddings_name in ("nq", "multiset")
	assert index_name in ("compressed", "exact", "no_index")
	kwargs["name"] = ".".join(name)
	super(WikiDprConfig, self).__init__(**kwargs)

	prefix = f"{wiki_split}.{embeddings_name}."
	if self.index_name == "exact":
	self.index_file = prefix + "HNSW128_SQ8-IP-{split}.faiss"
	else:
	self.index_file = prefix + "IVF4096_HNSW128_PQ128-IP-{split}.faiss"
	if self.dummy:
	self.index_file = "dummy." + self.index_file


	class WikiDpr(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIG_CLASS = WikiDprConfig
	BUILDER_CONFIGS = [
	WikiDprConfig(
	embeddings_name=embeddings_name,
	with_embeddings=with_embeddings,
	index_name=index_name,
	version=datasets.Version("0.0.0"),
	)
	for with_embeddings in (True, False)
	for embeddings_name in ("nq", "multiset")
	for index_name in ("exact", "compressed", "no_index")
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"title": datasets.Value("string"),
	"embeddings": datasets.Sequence(datasets.Value("float32")),
	}
	)
	if self.config.with_embeddings
	else datasets.Features(
	{"id": datasets.Value("string"), "text": datasets.Value("string"), "title": datasets.Value("string")}
	),
	supervised_keys=None,
	homepage="https://github.com/facebookresearch/DPR",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	files_to_download = {"data_file": _DATA_URL}
	downloaded_files = dl_manager.download_and_extract(files_to_download)
	if self.config.with_embeddings:
	vectors_url = _NQ_VECTORS_URL if self.config.embeddings_name == "nq" else _MULTISET_VECTORS_URL
	if self.config.dummy:
	downloaded_files["vectors_files"] = dl_manager.download([vectors_url.format(i=0)])
	else:
	downloaded_files["vectors_files"] = dl_manager.download([vectors_url.format(i=i) for i in range(50)])
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=downloaded_files),
	]

	def _generate_examples(self, data_file, vectors_files=None):
	vec_idx = 0
	vecs = []
	lines = open(data_file, "r", encoding="utf-8")
	next(lines) # skip headers
	for i, line in enumerate(lines):
	if self.config.dummy and i == 10000:
	break
	if i == 21015300:
	break # ignore the last 24 examples for which the embeddings are missing.
	id, text, title = line.strip().split("\t")
	text = text[1:-1] # remove " symbol at the beginning and the end
	text = text.replace('""', '"') # replace double quotes by simple quotes
	if self.config.with_embeddings:
	if vec_idx >= len(vecs):
	if len(vectors_files) == 0:
	logger.warning(f"Ran out of vector files at index {i}")
	break
	vecs = np.load(open(vectors_files.pop(0), "rb"), allow_pickle=True)
	vec_idx = 0
	vec_id, vec = vecs[vec_idx]
	assert int(id) == int(vec_id), f"ID mismatch between lines {id} and vector {vec_id}"
	yield id, {"id": id, "text": text, "title": title, "embeddings": vec}
	vec_idx += 1
	else:
	yield id, {
	"id": id,
	"text": text,
	"title": title,
	}

	def _post_processing_resources(self, split):
	if self.config.with_index:
	return {"embeddings_index": self.config.index_file.format(split=split)}
	else:
	return {}

	def _download_post_processing_resources(self, split, resource_name, dl_manager):
	if resource_name == "embeddings_index":
	try:
	downloaded_resources = dl_manager.download_and_extract(
	{"embeddings_index": _INDEX_URL + "/" + self.config.index_file.format(split=split)}
	)
	return downloaded_resources["embeddings_index"]
	except (FileNotFoundError, ConnectionError): # index doesn't exist
	pass

	def _post_process(self, dataset, resources_paths):
	if self.config.with_index:
	index_file = resources_paths["embeddings_index"]
	if os.path.exists(index_file):
	dataset.load_faiss_index("embeddings", index_file)
	else:
	if "embeddings" not in dataset.column_names:
	raise ValueError("Couldn't build the index because there are no embeddings.")
	import faiss

	d = 768
	train_size = self.config.index_train_size
	logger.info("Building wiki_dpr faiss index")
	if self.config.index_name == "exact":
	index = faiss.IndexHNSWSQ(d, faiss.ScalarQuantizer.QT_8bit, 128, faiss.METRIC_INNER_PRODUCT)
	index.hnsw.efConstruction = 200
	index.hnsw.efSearch = 128
	dataset.add_faiss_index("embeddings", custom_index=index, train_size=train_size)
	else:
	quantizer = faiss.IndexHNSWFlat(d, 128, faiss.METRIC_INNER_PRODUCT)
	quantizer.hnsw.efConstruction = 200
	quantizer.hnsw.efSearch = 128
	ivf_index = faiss.IndexIVFPQ(quantizer, d, 4096, 128, 8, faiss.METRIC_INNER_PRODUCT)
	ivf_index.nprobe = 64
	ivf_index.own_fields = True
	quantizer.this.disown()
	dataset.add_faiss_index(
	"embeddings",
	train_size=train_size,
	custom_index=ivf_index,
	)
	logger.info("Saving wiki_dpr faiss index")
	dataset.save_faiss_index("embeddings", index_file)
	return dataset