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Enable dataset viewer by hosting data files (#2)

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- Host data files (7bd3fa9ff4132bfbe0609813cc021585d35b86c2)
- Update loading script (bceaf96861dac37d68a292a1d51f83f47e4c4fec)
- Delete legacy metadata JSON file (2df9a65a59d2d09700524961b5322a002439c813)

data/GenericsKB-Best.tsv.gz ADDED
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data/GenericsKB-SimpleWiki-With-Context.jsonl.gz ADDED
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data/GenericsKB-Waterloo-With-Context.jsonl.gz ADDED
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data/GenericsKB.tsv.gz ADDED
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dataset_infos.json DELETED
@@ -1 +0,0 @@
1
- {"generics_kb_best": {"description": "The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as \"Dogs bark,\" and \"Trees remove carbon dioxide from the atmosphere.\" Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {GenericsKB: A Knowledge Base of Generic Statements},\nauthors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},\nyear={2020},\npublisher = {Allen Institute for AI},\n}\n", "homepage": "https://allenai.org/data/genericskb", "license": "cc-by-4.0", "features": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "term": {"dtype": "string", "id": null, "_type": "Value"}, "quantifier_frequency": {"dtype": "string", "id": null, "_type": "Value"}, "quantifier_number": {"dtype": "string", "id": null, "_type": "Value"}, "generic_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "score": {"dtype": "float64", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "generics_kb", "config_name": "generics_kb_best", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 99897719, "num_examples": 1020868, "dataset_name": "generics_kb"}}, "download_checksums": {"https://drive.google.com/u/0/uc?id=12DfIzoWyHIQqssgUgDvz3VG8_ScSh6ng&export=download": {"num_bytes": 94850525, "checksum": "0668b23c8b1579b6a76fcf48e04f3c9ea039ca9048a26848151d689deabb75e2"}}, "download_size": 94850525, "post_processing_size": null, "dataset_size": 99897719, "size_in_bytes": 194748244}, "generics_kb": {"description": "The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as \"Dogs bark,\" and \"Trees remove carbon dioxide from the atmosphere.\" Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {GenericsKB: A Knowledge Base of Generic Statements},\nauthors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},\nyear={2020},\npublisher = {Allen Institute for AI},\n}\n", "homepage": "https://allenai.org/data/genericskb", "license": "cc-by-4.0", "features": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "term": {"dtype": "string", "id": null, "_type": "Value"}, "quantifier_frequency": {"dtype": "string", "id": null, "_type": "Value"}, "quantifier_number": {"dtype": "string", "id": null, "_type": "Value"}, "generic_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "score": {"dtype": "float64", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "generics_kb", "config_name": "generics_kb", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 348158966, "num_examples": 3433000, "dataset_name": "generics_kb"}}, "download_checksums": {"https://drive.google.com/u/0/uc?id=1UOIEzQTid7SzKx2tbwSSPxl7g-CjpoZa&export=download": {"num_bytes": 343284785, "checksum": "7ec2419e700b3425129032f75f0bb01887bdb84231526468751d6cc2a9b9e61e"}}, "download_size": 343284785, "post_processing_size": null, "dataset_size": 348158966, "size_in_bytes": 691443751}, "generics_kb_simplewiki": {"description": "The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as \"Dogs bark,\" and \"Trees remove carbon dioxide from the atmosphere.\" Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {GenericsKB: A Knowledge Base of Generic Statements},\nauthors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},\nyear={2020},\npublisher = {Allen Institute for AI},\n}\n", "homepage": "https://allenai.org/data/genericskb", "license": "cc-by-4.0", "features": {"source_name": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentences_before": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sentences_after": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "concept_name": {"dtype": "string", "id": null, "_type": "Value"}, "quantifiers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "id": {"dtype": "string", "id": null, "_type": "Value"}, "bert_score": {"dtype": "float64", "id": null, "_type": "Value"}, "headings": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "categories": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "generics_kb", "config_name": "generics_kb_simplewiki", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10039355, "num_examples": 12765, "dataset_name": "generics_kb"}}, "download_checksums": {"https://drive.google.com/u/0/uc?id=1SpN9Qc7XRy5xs4tIfXkcLOEAP2IVaK15&export=download": {"num_bytes": 16437369, "checksum": "f6c0da9c9100172e8979907448497717d8ea1a50ee96aa2b81e447423c6cd0bb"}}, "download_size": 16437369, "post_processing_size": null, "dataset_size": 10039355, "size_in_bytes": 26476724}, "generics_kb_waterloo": {"description": "The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as \"Dogs bark,\" and \"Trees remove carbon dioxide from the atmosphere.\" Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {GenericsKB: A Knowledge Base of Generic Statements},\nauthors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},\nyear={2020},\npublisher = {Allen Institute for AI},\n}\n", "homepage": "https://allenai.org/data/genericskb", "license": "cc-by-4.0", "features": {"source_name": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentences_before": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sentences_after": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "concept_name": {"dtype": "string", "id": null, "_type": "Value"}, "quantifiers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "id": {"dtype": "string", "id": null, "_type": "Value"}, "bert_score": {"dtype": "float64", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "generics_kb", "config_name": "generics_kb_waterloo", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4277214701, "num_examples": 3666725, "dataset_name": "generics_kb"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 4277214701, "size_in_bytes": 4277214701}}
 
 
generics_kb.py CHANGED
@@ -17,7 +17,6 @@
17
 
18
  import ast
19
  import csv
20
- import os
21
 
22
  import datasets
23
 
@@ -41,13 +40,13 @@ _HOMEPAGE = "https://allenai.org/data/genericskb"
41
 
42
  _LICENSE = "cc-by-4.0"
43
 
44
- _URL = "https://drive.google.com/u/0/uc?id={0}&export=download"
45
 
46
- _FILEPATHS = {
47
- "generics_kb_best": _URL.format("12DfIzoWyHIQqssgUgDvz3VG8_ScSh6ng"),
48
- "generics_kb": _URL.format("1UOIEzQTid7SzKx2tbwSSPxl7g-CjpoZa"),
49
- "generics_kb_simplewiki": _URL.format("1SpN9Qc7XRy5xs4tIfXkcLOEAP2IVaK15"),
50
- "generics_kb_waterloo": "cskb-waterloo-06-21-with-bert-scores.jsonl",
51
  }
52
 
53
 
@@ -60,7 +59,7 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
60
  datasets.BuilderConfig(
61
  name="generics_kb_best",
62
  version=VERSION,
63
- description="This is the default and recommended config.Comprises of GENERICSKB generics with a score > 0.234 ",
64
  ),
65
  datasets.BuilderConfig(
66
  name="generics_kb", version=VERSION, description="This GENERICSKB that contains 3,433,000 sentences."
@@ -77,30 +76,10 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
77
  ),
78
  ]
79
 
80
- @property
81
- def manual_download_instructions(self):
82
- return """\
83
- You need to manually download the files needed for the dataset config generics_kb_waterloo. The other configs like generics_kb_best don't need manual downloads.
84
- The <path/to/folder> can e.g. be `~/Downloads/GenericsKB`. Download the following required files from https://drive.google.com/drive/folders/1vqfVXhJXJWuiiXbUa4rZjOgQoJvwZUoT
85
- For working on "generics_kb_waterloo" data,
86
- 1. Manually download 'GenericsKB-Waterloo-WithContext.jsonl.zip' into your <path/to/folder>.Please ensure the filename is as is.
87
- The Waterloo is also generics from GenericsKB.tsv, but expanded to also include their surrounding context (before/after sentences). The Waterloo generics are the majority of GenericsKB. This zip file is 1.4GB expanding to 5.5GB.
88
- 2. Extract the GenericsKB-Waterloo-WithContext.jsonl.zip; It will create a file of 5.5 GB called cskb-waterloo-06-21-with-bert-scores.jsonl.
89
- Ensure you move this file into your <path/to/folder>.
90
-
91
- generics_kb can then be loaded using the following commands based on which data you want to work on. Data files must be present in the <path/to/folder> if using "generics_kb_waterloo" config.
92
- 1. `datasets.load_dataset("generics_kb","generics_kb_best")`.
93
- 2. `datasets.load_dataset("generics_kb","generics_kb")`
94
- 3. `datasets.load_dataset("generics_kb","generics_kb_simplewiki")`
95
- 4. `datasets.load_dataset("generics_kb","generics_kb_waterloo", data_dir="<path/to/folder>")`
96
-
97
- """
98
-
99
  DEFAULT_CONFIG_NAME = "generics_kb_best"
100
 
101
  def _info(self):
102
  if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
103
-
104
  featuredict = {
105
  "source_name": datasets.Value("string"),
106
  "sentence": datasets.Value("string"),
@@ -118,7 +97,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
118
  features = datasets.Features(featuredict)
119
 
120
  else:
121
-
122
  features = datasets.Features(
123
  {
124
  "source": datasets.Value("string"),
@@ -148,24 +126,7 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
148
  )
149
 
150
  def _split_generators(self, dl_manager):
151
-
152
- if self.config.name == "generics_kb_waterloo":
153
- data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
154
- # check if manual folder exists
155
- if not os.path.exists(data_dir):
156
- raise FileNotFoundError(
157
- f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('generics_kb', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})"
158
- )
159
-
160
- # Check if required files exist in the folder
161
- filepath = os.path.join(data_dir, _FILEPATHS[self.config.name])
162
-
163
- if not os.path.exists(filepath):
164
- raise FileNotFoundError(
165
- f"{filepath} does not exist. Make sure you required files are present in {data_dir} `. Manual download instructions: {self.manual_download_instructions})"
166
- )
167
- else:
168
- filepath = dl_manager.download(_FILEPATHS[self.config.name])
169
 
170
  return [
171
  datasets.SplitGenerator(
@@ -181,7 +142,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
181
  """Yields examples."""
182
 
183
  if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
184
-
185
  with open(filepath, encoding="utf-8") as f:
186
  for id_, row in enumerate(f):
187
  data = ast.literal_eval(row)
@@ -202,7 +162,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
202
 
203
  yield id_, result
204
  else:
205
-
206
  with open(filepath, encoding="utf-8") as f:
207
  # Skip the header
208
  next(f)
@@ -210,7 +169,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
210
  read_tsv = csv.reader(f, delimiter="\t")
211
 
212
  for id_, row in enumerate(read_tsv):
213
-
214
  quantifier = row[2]
215
  quantifier_frequency = ""
216
  quantifier_number = ""
 
17
 
18
  import ast
19
  import csv
 
20
 
21
  import datasets
22
 
 
40
 
41
  _LICENSE = "cc-by-4.0"
42
 
43
+ _BASE_URL = "data/{0}"
44
 
45
+ _URLS = {
46
+ "generics_kb_best": _BASE_URL.format("GenericsKB-Best.tsv.gz"),
47
+ "generics_kb": _BASE_URL.format("GenericsKB.tsv.gz"),
48
+ "generics_kb_simplewiki": _BASE_URL.format("GenericsKB-SimpleWiki-With-Context.jsonl.gz"),
49
+ "generics_kb_waterloo": _BASE_URL.format("GenericsKB-Waterloo-With-Context.jsonl.gz"),
50
  }
51
 
52
 
 
59
  datasets.BuilderConfig(
60
  name="generics_kb_best",
61
  version=VERSION,
62
+ description="This is the default and recommended config. Comprises of GENERICSKB generics with a score > 0.234 ",
63
  ),
64
  datasets.BuilderConfig(
65
  name="generics_kb", version=VERSION, description="This GENERICSKB that contains 3,433,000 sentences."
 
76
  ),
77
  ]
78
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
79
  DEFAULT_CONFIG_NAME = "generics_kb_best"
80
 
81
  def _info(self):
82
  if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
 
83
  featuredict = {
84
  "source_name": datasets.Value("string"),
85
  "sentence": datasets.Value("string"),
 
97
  features = datasets.Features(featuredict)
98
 
99
  else:
 
100
  features = datasets.Features(
101
  {
102
  "source": datasets.Value("string"),
 
126
  )
127
 
128
  def _split_generators(self, dl_manager):
129
+ filepath = dl_manager.download_and_extract(_URLS[self.config.name])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
130
 
131
  return [
132
  datasets.SplitGenerator(
 
142
  """Yields examples."""
143
 
144
  if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
 
145
  with open(filepath, encoding="utf-8") as f:
146
  for id_, row in enumerate(f):
147
  data = ast.literal_eval(row)
 
162
 
163
  yield id_, result
164
  else:
 
165
  with open(filepath, encoding="utf-8") as f:
166
  # Skip the header
167
  next(f)
 
169
  read_tsv = csv.reader(f, delimiter="\t")
170
 
171
  for id_, row in enumerate(read_tsv):
 
172
  quantifier = row[2]
173
  quantifier_frequency = ""
174
  quantifier_number = ""