Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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dataset_infos.json

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+                "name": "train",
+                "num_bytes": 37322605,
+                "num_examples": 10000,
+                "dataset_name": "wiki_dpr"
+            }
+        },
+        "download_checksums": {
+            "https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz": {
+                "num_bytes": 4694541059,
+                "checksum": "c39b020c855a2b5c25ffef3abe4a3b6f9b829ad7dbc14ec3d163d34d7c53ea8d"
+            },
+            "https://dl.fbaipublicfiles.com/dpr/data/wiki_encoded/single/nq/wiki_passages_0": {
+                "num_bytes": 1324693633,
+                "checksum": "c277bc2ba1ef027ee86cb24b1df7015aaa32972da88d5183f6e5a9563d8705b5"
+            }
+        },
+        "download_size": 6019234692,
+        "dataset_size": 37322605,
+        "size_in_bytes": 6056557297
+    },
+    "psgs_w100_no_embeddings": {
+        "description": "\nThis is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.\nIt contains 21M passages from wikipedia along with their DPR embeddings.\nThe wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.\n",
+        "citation": "\n@misc{karpukhin2020dense,\n    title={Dense Passage Retrieval for Open-Domain Question Answering},\n    author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},\n    year={2020},\n    eprint={2004.04906},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n",
+        "homepage": "https://github.com/facebookresearch/DPR",
+        "license": "",
+        "features": {
+            "id": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "text": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "title": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            }
+        },
+        "supervised_keys": null,
+        "builder_name": "wiki_dpr",
+        "config_name": "psgs_w100_no_embeddings",
+        "version": {
+            "version_str": "1.0.0",
+            "description": null,
+            "datasets_version_to_prepare": null,
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 13776210580,
+                "num_examples": 21015300,
+                "dataset_name": "wiki_dpr"
+            }
+        },
+        "download_checksums": {
+            "https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz": {
+                "num_bytes": 4694541059,
+                "checksum": "c39b020c855a2b5c25ffef3abe4a3b6f9b829ad7dbc14ec3d163d34d7c53ea8d"
+            }
+        },
+        "download_size": 4694541059,
+        "dataset_size": 13776210580,
+        "size_in_bytes": 18470751639
+    }
+}

dummy/0.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21f78c610a9931eba911716ed4d8b0d28357ed97146156de58936737961c52f7
+size 1909

wiki_dpr.py

@@ -0,0 +1,190 @@
+import logging
+import os
+
+import numpy as np
+
+import datasets
+
+
+_CITATION = """
+@misc{karpukhin2020dense,
+    title={Dense Passage Retrieval for Open-Domain Question Answering},
+    author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
+    year={2020},
+    eprint={2004.04906},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_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"
+
+_VECTORS_URL = "https://dl.fbaipublicfiles.com/dpr/data/wiki_encoded/single/nq/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 trained on NQ.
+            with_index (`bool`, defaults to `True`): Load the faiss index trained on the embeddings.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        self.with_embeddings = with_embeddings
+        self.with_index = with_index
+        self.wiki_split = wiki_split
+        self.embeddings_name = embeddings_name if with_embeddings else "no_embeddings"
+        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 self.dummy:
+            name = ["dummy"] + name
+            assert (
+                self.index_name != "compressed" or not self.with_index
+            ), "Please use `index_name='exact' for dummy wiki_dpr`"
+        kwargs["name"] = ".".join(name)
+        super(WikiDprConfig, self).__init__(**kwargs)
+
+        if self.index_name == "exact":
+            self.index_file = "psgs_w100.nq.IndexHNSWFlat-IP-{split}.faiss"
+        else:
+            self.index_file = "psgs_w100.nq.IVFPQ4096_HNSW32_PQ64-IP-{split}.faiss"
+        if self.dummy:
+            self.index_file = "dummy." + self.index_file
+
+
+class WikiDpr(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = WikiDprConfig
+
+    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:
+            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:
+                        logging.warning("Ran out of vector files at index {}".format(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), "ID mismatch between lines {} and vector {}".format(id, 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": os.path.join(_INDEX_URL, self.config.index_file.format(split=split))}
+                )
+                return downloaded_resources["embeddings_index"]
+            except 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
+
+                train_size = self.config.index_train_size
+                logging.info("Building wiki_dpr faiss index")
+                if self.config.index_name == "exact":
+                    d = 768
+                    index = faiss.IndexHNSWFlat(d, 512, faiss.METRIC_INNER_PRODUCT)
+                    dataset.add_faiss_index("embeddings", custom_index=index)
+                else:
+                    d = 768
+                    quantizer = faiss.IndexHNSWFlat(d, 32, faiss.METRIC_INNER_PRODUCT)
+                    ivf_index = faiss.IndexIVFPQ(quantizer, d, 4096, 64, 8, faiss.METRIC_INNER_PRODUCT)
+                    ivf_index.own_fields = True
+                    quantizer.this.disown()
+                    dataset.add_faiss_index(
+                        "embeddings",
+                        train_size=train_size,
+                        custom_index=ivf_index,
+                    )
+                logging.info("Saving wiki_dpr faiss index")
+                dataset.save_faiss_index("embeddings", index_file)
+        return dataset