Enable dataset viewer by hosting data files (#2)

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

data/GenericsKB-Best.tsv.gz

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data/GenericsKB-SimpleWiki-With-Context.jsonl.gz

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+size 2568536

data/GenericsKB-Waterloo-With-Context.jsonl.gz

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+size 1513320915

data/GenericsKB.tsv.gz

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+size 101859440

dataset_infos.json

@@ -1 +0,0 @@
-{"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

@@ -17,7 +17,6 @@
 
 import ast
 import csv
-import os
 
 import datasets
 
@@ -41,13 +40,13 @@ _HOMEPAGE = "https://allenai.org/data/genericskb"
 
 _LICENSE = "cc-by-4.0"
 
-_URL = "https://drive.google.com/u/0/uc?id={0}&export=download"
+_BASE_URL = "data/{0}"
 
-_FILEPATHS = {
-    "generics_kb_best": _URL.format("12DfIzoWyHIQqssgUgDvz3VG8_ScSh6ng"),
-    "generics_kb": _URL.format("1UOIEzQTid7SzKx2tbwSSPxl7g-CjpoZa"),
-    "generics_kb_simplewiki": _URL.format("1SpN9Qc7XRy5xs4tIfXkcLOEAP2IVaK15"),
-    "generics_kb_waterloo": "cskb-waterloo-06-21-with-bert-scores.jsonl",
+_URLS = {
+    "generics_kb_best": _BASE_URL.format("GenericsKB-Best.tsv.gz"),
+    "generics_kb": _BASE_URL.format("GenericsKB.tsv.gz"),
+    "generics_kb_simplewiki": _BASE_URL.format("GenericsKB-SimpleWiki-With-Context.jsonl.gz"),
+    "generics_kb_waterloo": _BASE_URL.format("GenericsKB-Waterloo-With-Context.jsonl.gz"),
 }
 
 
@@ -60,7 +59,7 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
         datasets.BuilderConfig(
             name="generics_kb_best",
             version=VERSION,
-            description="This is the default and recommended config.Comprises of GENERICSKB generics with a score > 0.234 ",
+            description="This is the default and recommended config. Comprises of GENERICSKB generics with a score > 0.234 ",
         ),
         datasets.BuilderConfig(
             name="generics_kb", version=VERSION, description="This GENERICSKB that contains 3,433,000 sentences."
@@ -77,30 +76,10 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
         ),
     ]
 
-    @property
-    def manual_download_instructions(self):
-        return """\
-      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.
-      The <path/to/folder> can e.g. be `~/Downloads/GenericsKB`. Download the following required files from https://drive.google.com/drive/folders/1vqfVXhJXJWuiiXbUa4rZjOgQoJvwZUoT
-      For working on "generics_kb_waterloo" data,
-        1. Manually download 'GenericsKB-Waterloo-WithContext.jsonl.zip' into your <path/to/folder>.Please ensure the filename is as is.
-           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.
-        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.
-           Ensure you move this file into your <path/to/folder>.
-
-      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.
-      1. `datasets.load_dataset("generics_kb","generics_kb_best")`.
-      2. `datasets.load_dataset("generics_kb","generics_kb")`
-      3. `datasets.load_dataset("generics_kb","generics_kb_simplewiki")`
-      4. `datasets.load_dataset("generics_kb","generics_kb_waterloo", data_dir="<path/to/folder>")`
-
-      """
-
     DEFAULT_CONFIG_NAME = "generics_kb_best"
 
     def _info(self):
         if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
-
             featuredict = {
                 "source_name": datasets.Value("string"),
                 "sentence": datasets.Value("string"),
@@ -118,7 +97,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
             features = datasets.Features(featuredict)
 
         else:
-
             features = datasets.Features(
                 {
                     "source": datasets.Value("string"),
@@ -148,24 +126,7 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-
-        if self.config.name == "generics_kb_waterloo":
-            data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
-            # check if manual folder exists
-            if not os.path.exists(data_dir):
-                raise FileNotFoundError(
-                    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})"
-                )
-
-            # Check if required files exist in the folder
-            filepath = os.path.join(data_dir, _FILEPATHS[self.config.name])
-
-            if not os.path.exists(filepath):
-                raise FileNotFoundError(
-                    f"{filepath} does not exist. Make sure you required files are present in {data_dir} `. Manual download instructions: {self.manual_download_instructions})"
-                )
-        else:
-            filepath = dl_manager.download(_FILEPATHS[self.config.name])
+        filepath = dl_manager.download_and_extract(_URLS[self.config.name])
 
         return [
             datasets.SplitGenerator(
@@ -181,7 +142,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
         """Yields examples."""
 
         if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
-
             with open(filepath, encoding="utf-8") as f:
                 for id_, row in enumerate(f):
                     data = ast.literal_eval(row)
@@ -202,7 +162,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
 
                     yield id_, result
         else:
-
             with open(filepath, encoding="utf-8") as f:
                 # Skip the header
                 next(f)
@@ -210,7 +169,6 @@ class GenericsKb(datasets.GeneratorBasedBuilder):
                 read_tsv = csv.reader(f, delimiter="\t")
 
                 for id_, row in enumerate(read_tsv):
-
                     quantifier = row[2]
                     quantifier_frequency = ""
                     quantifier_number = ""