Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

dataset_infos.json

@@ -1,430 +1 @@
-{
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dummy/psgs_w100.multiset.compressed/0.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e51b03af8523f614fb7d4872f252a8a7a8720bd7202c5462545552b9b2289f89
+size 1921

dummy/psgs_w100.multiset.exact/0.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58d80a21bce46bb5243bc8749a2685f9b20ade1e6e1d840c86910bad121fa616
+size 1901

dummy/psgs_w100.multiset.no_index/0.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:462f6e366ff9bd80c684570a532d7c004e69c9dd547fd7e7c5d9a011dd18c877
+size 1609

dummy/{0.0.0 → psgs_w100.nq.compressed/0.0.0}/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:21f78c610a9931eba911716ed4d8b0d28357ed97146156de58936737961c52f7
+oid sha256:80f9821b503cbe080f6511c3140ad2e3c1ea291091ad22aa5fe4959bf0163c4e
 size 1909

dummy/psgs_w100.nq.exact/0.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80b0b79c479089caed5c4063d0aba70239e207206b1c3e1dabd6953d7e613dd9
+size 1889

dummy/psgs_w100.nq.no_index/0.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:462f6e366ff9bd80c684570a532d7c004e69c9dd547fd7e7c5d9a011dd18c877
+size 1609

wiki_dpr.py

@@ -27,7 +27,9 @@ _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}"
+_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"
 
@@ -48,14 +50,19 @@ class WikiDprConfig(datasets.BuilderConfig):
     ):
         """BuilderConfig for WikiSnippets.
         Args:
-            with_embeddings (`bool`, defaults to `True`): Load the 768-dimensional embeddings from DPR trained on NQ.
+            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
         self.wiki_split = wiki_split
-        self.embeddings_name = embeddings_name if with_embeddings else "no_embeddings"
+        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
@@ -65,19 +72,33 @@ class WikiDprConfig(datasets.BuilderConfig):
             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 = "psgs_w100.nq.IndexHNSWFlat-IP-{split}.faiss"
+            self.index_file = prefix + "HNSW128_SQ8-IP-{split}.faiss"
         else:
-            self.index_file = "psgs_w100.nq.IVFPQ4096_HNSW32_PQ64-IP-{split}.faiss"
+            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_index=(index_name != "no_index"),
+            index_name=index_name,
+            version=datasets.Version("0.0.0"),
+        )
+        for embeddings_name in ("nq", "multiset")
+        for index_name in ("exact", "compressed", "no_index")
+    ]
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -103,10 +124,11 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
         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)])
+                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)])
+                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),
         ]
@@ -152,7 +174,7 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
         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))}
+                    {"embeddings_index": _INDEX_URL + "/" + self.config.index_file.format(split=split)}
                 )
                 return downloaded_resources["embeddings_index"]
             except (FileNotFoundError, ConnectionError):  # index doesn't exist
@@ -168,16 +190,20 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
                     raise ValueError("Couldn't build the index because there are no embeddings.")
                 import faiss
 
+                d = 768
                 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)
+                    index = faiss.IndexHNSWFlat(d, 128, faiss.METRIC_INNER_PRODUCT)
+                    index.hnsw.efConstruction = 200
+                    index.hnsw.efSearch = 128
                     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)
+                    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(