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Datasets:
facebook
/
wiki_dpr

Tasks:
Fill-Mask
Text Generation
Sub-tasks:
language-modeling
masked-language-modeling
Languages:
English
Size:
10M<n<100M
ArXiv:
Tags:
text-search
License:
Dataset card Files Community
15
system HF staff commited on
Commit
cdb0294
·
1 Parent(s): f775f65

Update files from the datasets library (from 1.2.0)

Browse files

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

Files changed (8) hide show
  1. dataset_infos.json +1 -430
  2. dummy/psgs_w100.multiset.compressed/0.0.0/dummy_data.zip +3 -0
  3. dummy/psgs_w100.multiset.exact/0.0.0/dummy_data.zip +3 -0
  4. dummy/psgs_w100.multiset.no_index/0.0.0/dummy_data.zip +3 -0
  5. dummy/{0.0.0 → psgs_w100.nq.compressed/0.0.0}/dummy_data.zip +1 -1
  6. dummy/psgs_w100.nq.exact/0.0.0/dummy_data.zip +3 -0
  7. dummy/psgs_w100.nq.no_index/0.0.0/dummy_data.zip +3 -0
  8. wiki_dpr.py +39 -13
dataset_infos.json CHANGED
@@ -1,430 +1 @@
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dummy/psgs_w100.multiset.compressed/0.0.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e51b03af8523f614fb7d4872f252a8a7a8720bd7202c5462545552b9b2289f89
3
+ size 1921
dummy/psgs_w100.multiset.exact/0.0.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:58d80a21bce46bb5243bc8749a2685f9b20ade1e6e1d840c86910bad121fa616
3
+ size 1901
dummy/psgs_w100.multiset.no_index/0.0.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:462f6e366ff9bd80c684570a532d7c004e69c9dd547fd7e7c5d9a011dd18c877
3
+ size 1609
dummy/{0.0.0 → psgs_w100.nq.compressed/0.0.0}/dummy_data.zip RENAMED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:21f78c610a9931eba911716ed4d8b0d28357ed97146156de58936737961c52f7
3
  size 1909
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:80f9821b503cbe080f6511c3140ad2e3c1ea291091ad22aa5fe4959bf0163c4e
3
  size 1909
dummy/psgs_w100.nq.exact/0.0.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:80b0b79c479089caed5c4063d0aba70239e207206b1c3e1dabd6953d7e613dd9
3
+ size 1889
dummy/psgs_w100.nq.no_index/0.0.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:462f6e366ff9bd80c684570a532d7c004e69c9dd547fd7e7c5d9a011dd18c877
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+ size 1609
wiki_dpr.py CHANGED
@@ -27,7 +27,9 @@ _LICENSE = """DPR is CC-BY-NC 4.0 licensed."""
27
 
28
  _DATA_URL = "https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz"
29
 
30
- _VECTORS_URL = "https://dl.fbaipublicfiles.com/dpr/data/wiki_encoded/single/nq/wiki_passages_{i}"
 
 
31
 
32
  _INDEX_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr"
33
 
@@ -48,14 +50,19 @@ class WikiDprConfig(datasets.BuilderConfig):
48
  ):
49
  """BuilderConfig for WikiSnippets.
50
  Args:
51
- with_embeddings (`bool`, defaults to `True`): Load the 768-dimensional embeddings from DPR trained on NQ.
52
  with_index (`bool`, defaults to `True`): Load the faiss index trained on the embeddings.
 
 
 
 
 
53
  **kwargs: keyword arguments forwarded to super.
54
  """
55
  self.with_embeddings = with_embeddings
56
  self.with_index = with_index
57
  self.wiki_split = wiki_split
58
- self.embeddings_name = embeddings_name if with_embeddings else "no_embeddings"
59
  self.index_name = index_name if with_index else "no_index"
60
  self.index_train_size = index_train_size
61
  self.dummy = dummy
@@ -65,19 +72,33 @@ class WikiDprConfig(datasets.BuilderConfig):
65
  assert (
66
  self.index_name != "compressed" or not self.with_index
67
  ), "Please use `index_name='exact' for dummy wiki_dpr`"
 
 
 
68
  kwargs["name"] = ".".join(name)
69
  super(WikiDprConfig, self).__init__(**kwargs)
70
 
 
71
  if self.index_name == "exact":
72
- self.index_file = "psgs_w100.nq.IndexHNSWFlat-IP-{split}.faiss"
73
  else:
74
- self.index_file = "psgs_w100.nq.IVFPQ4096_HNSW32_PQ64-IP-{split}.faiss"
75
  if self.dummy:
76
  self.index_file = "dummy." + self.index_file
77
 
78
 
79
  class WikiDpr(datasets.GeneratorBasedBuilder):
80
  BUILDER_CONFIG_CLASS = WikiDprConfig
 
 
 
 
 
 
 
 
 
 
81
 
82
  def _info(self):
83
  return datasets.DatasetInfo(
@@ -103,10 +124,11 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
103
  files_to_download = {"data_file": _DATA_URL}
104
  downloaded_files = dl_manager.download_and_extract(files_to_download)
105
  if self.config.with_embeddings:
 
106
  if self.config.dummy:
107
- downloaded_files["vectors_files"] = dl_manager.download([_VECTORS_URL.format(i=0)])
108
  else:
109
- downloaded_files["vectors_files"] = dl_manager.download([_VECTORS_URL.format(i=i) for i in range(50)])
110
  return [
111
  datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=downloaded_files),
112
  ]
@@ -152,7 +174,7 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
152
  if resource_name == "embeddings_index":
153
  try:
154
  downloaded_resources = dl_manager.download_and_extract(
155
- {"embeddings_index": os.path.join(_INDEX_URL, self.config.index_file.format(split=split))}
156
  )
157
  return downloaded_resources["embeddings_index"]
158
  except (FileNotFoundError, ConnectionError): # index doesn't exist
@@ -168,16 +190,20 @@ class WikiDpr(datasets.GeneratorBasedBuilder):
168
  raise ValueError("Couldn't build the index because there are no embeddings.")
169
  import faiss
170
 
 
171
  train_size = self.config.index_train_size
172
  logging.info("Building wiki_dpr faiss index")
173
  if self.config.index_name == "exact":
174
- d = 768
175
- index = faiss.IndexHNSWFlat(d, 512, faiss.METRIC_INNER_PRODUCT)
 
176
  dataset.add_faiss_index("embeddings", custom_index=index)
177
  else:
178
- d = 768
179
- quantizer = faiss.IndexHNSWFlat(d, 32, faiss.METRIC_INNER_PRODUCT)
180
- ivf_index = faiss.IndexIVFPQ(quantizer, d, 4096, 64, 8, faiss.METRIC_INNER_PRODUCT)
 
 
181
  ivf_index.own_fields = True
182
  quantizer.this.disown()
183
  dataset.add_faiss_index(
 
27
 
28
  _DATA_URL = "https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz"
29
 
30
+ _NQ_VECTORS_URL = "https://dl.fbaipublicfiles.com/dpr/data/wiki_encoded/single/nq/wiki_passages_{i}"
31
+
32
+ _MULTISET_VECTORS_URL = "https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_{i}"
33
 
34
  _INDEX_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr"
35
 
 
50
  ):
51
  """BuilderConfig for WikiSnippets.
52
  Args:
53
+ with_embeddings (`bool`, defaults to `True`): Load the 768-dimensional embeddings from DPR.
54
  with_index (`bool`, defaults to `True`): Load the faiss index trained on the embeddings.
55
+ wiki_split (`str`, defaults to `psgs_w100`): name of the splitting method of wiki articles.
56
+ embeddings_name (`str`, defaults to `nq`): "nq" or "multiset", depending on which dataset DPR was trained on.
57
+ index_name (`str`, defaults to `compressed`): "compressed" or "exact", the configuration of the faiss index to use.
58
+ index_train_size (`int`, defaults to `262144`): Size of the subset to use to train the index, if it is trainable.
59
+ dummy (`bool`, defaults to `False`): Dummy uses only 10 000 examples for testing purposes.
60
  **kwargs: keyword arguments forwarded to super.
61
  """
62
  self.with_embeddings = with_embeddings
63
  self.with_index = with_index
64
  self.wiki_split = wiki_split
65
+ self.embeddings_name = embeddings_name
66
  self.index_name = index_name if with_index else "no_index"
67
  self.index_train_size = index_train_size
68
  self.dummy = dummy
 
72
  assert (
73
  self.index_name != "compressed" or not self.with_index
74
  ), "Please use `index_name='exact' for dummy wiki_dpr`"
75
+ assert wiki_split == "psgs_w100"
76
+ assert embeddings_name in ("nq", "multiset")
77
+ assert index_name in ("compressed", "exact", "no_index")
78
  kwargs["name"] = ".".join(name)
79
  super(WikiDprConfig, self).__init__(**kwargs)
80
 
81
+ prefix = f"{wiki_split}.{embeddings_name}."
82
  if self.index_name == "exact":
83
+ self.index_file = prefix + "HNSW128_SQ8-IP-{split}.faiss"
84
  else:
85
+ self.index_file = prefix + "IVF4096_HNSW128_PQ128-IP-{split}.faiss"
86
  if self.dummy:
87
  self.index_file = "dummy." + self.index_file
88
 
89
 
90
  class WikiDpr(datasets.GeneratorBasedBuilder):
91
  BUILDER_CONFIG_CLASS = WikiDprConfig
92
+ BUILDER_CONFIGS = [
93
+ WikiDprConfig(
94
+ embeddings_name=embeddings_name,
95
+ with_index=(index_name != "no_index"),
96
+ index_name=index_name,
97
+ version=datasets.Version("0.0.0"),
98
+ )
99
+ for embeddings_name in ("nq", "multiset")
100
+ for index_name in ("exact", "compressed", "no_index")
101
+ ]
102
 
103
  def _info(self):
104
  return datasets.DatasetInfo(
 
124
  files_to_download = {"data_file": _DATA_URL}
125
  downloaded_files = dl_manager.download_and_extract(files_to_download)
126
  if self.config.with_embeddings:
127
+ vectors_url = _NQ_VECTORS_URL if self.config.embeddings_name == "nq" else _MULTISET_VECTORS_URL
128
  if self.config.dummy:
129
+ downloaded_files["vectors_files"] = dl_manager.download([vectors_url.format(i=0)])
130
  else:
131
+ downloaded_files["vectors_files"] = dl_manager.download([vectors_url.format(i=i) for i in range(50)])
132
  return [
133
  datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=downloaded_files),
134
  ]
 
174
  if resource_name == "embeddings_index":
175
  try:
176
  downloaded_resources = dl_manager.download_and_extract(
177
+ {"embeddings_index": _INDEX_URL + "/" + self.config.index_file.format(split=split)}
178
  )
179
  return downloaded_resources["embeddings_index"]
180
  except (FileNotFoundError, ConnectionError): # index doesn't exist
 
190
  raise ValueError("Couldn't build the index because there are no embeddings.")
191
  import faiss
192
 
193
+ d = 768
194
  train_size = self.config.index_train_size
195
  logging.info("Building wiki_dpr faiss index")
196
  if self.config.index_name == "exact":
197
+ index = faiss.IndexHNSWFlat(d, 128, faiss.METRIC_INNER_PRODUCT)
198
+ index.hnsw.efConstruction = 200
199
+ index.hnsw.efSearch = 128
200
  dataset.add_faiss_index("embeddings", custom_index=index)
201
  else:
202
+ quantizer = faiss.IndexHNSWFlat(d, 128, faiss.METRIC_INNER_PRODUCT)
203
+ quantizer.hnsw.efConstruction = 200
204
+ quantizer.hnsw.efSearch = 128
205
+ ivf_index = faiss.IndexIVFPQ(quantizer, d, 4096, 128, 8, faiss.METRIC_INNER_PRODUCT)
206
+ ivf_index.nprobe = 64
207
  ivf_index.own_fields = True
208
  quantizer.this.disown()
209
  dataset.add_faiss_index(