Convert dataset to Parquet (#4)

- Convert dataset to Parquet (06cde0518cfc5d17362fc6e6b07983eb355eba19)
- Add NE data files (97a933d53d203e785e6f78cba54915661d45e94b)
- Add P data files (3df944cd17642a66ef280973e6ab65064265cd2d)
- Add V data files (004c195ebcedc5291e47c5502497705fc278930a)
- Add raw data files (a731474f5e462bb74d650687391cd0d6e87659f2)
- Delete legacy dataset_infos.json (3298b4f6fa9e56d0acfc5771d28369d4c645b814)
- Delete loading script (c0c7e39da5ac04080220da735903622498477c88)
- Delete data file (e91ed131e95b8ac63bdbaea446216033157c74c9)

data/CBTest.tgz → CN/test-00000-of-00001.parquet

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+size 2091183

CN/train-00000-of-00001.parquet

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P/test-00000-of-00001.parquet

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P/train-00000-of-00002.parquet

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README.md

@@ -1,5 +1,4 @@
 ---
-pretty_name: Children’s Book Test (CBT)
 annotations_creators:
 - machine-generated
 language_creators:
@@ -21,26 +20,37 @@ task_categories:
 task_ids:
 - multiple-choice-qa
 paperswithcode_id: cbt
+pretty_name: Children’s Book Test (CBT)
+config_names:
+- CN
+- NE
+- P
+- V
+- raw
 dataset_info:
-- config_name: raw
+- config_name: CN
   features:
-  - name: title
+  - name: sentences
+    sequence: string
+  - name: question
     dtype: string
-  - name: content
+  - name: answer
     dtype: string
+  - name: options
+    sequence: string
   splits:
   - name: train
-    num_bytes: 25741580
-    num_examples: 98
+    num_bytes: 301730151
+    num_examples: 120769
   - name: test
-    num_bytes: 1528704
-    num_examples: 5
+    num_bytes: 6138376
+    num_examples: 2500
   - name: validation
-    num_bytes: 1182657
-    num_examples: 5
-  download_size: 120547669
-  dataset_size: 28452941
-- config_name: V
+    num_bytes: 4737257
+    num_examples: 2000
+  download_size: 31615166
+  dataset_size: 312605784
+- config_name: NE
   features:
   - name: sentences
     sequence: string
@@ -52,16 +62,16 @@ dataset_info:
     sequence: string
   splits:
   - name: train
-    num_bytes: 252177649
-    num_examples: 105825
+    num_bytes: 253551931
+    num_examples: 108719
   - name: test
-    num_bytes: 5806625
+    num_bytes: 5707734
     num_examples: 2500
   - name: validation
-    num_bytes: 4556425
+    num_bytes: 4424316
     num_examples: 2000
-  download_size: 120547669
-  dataset_size: 262540699
+  download_size: 29693075
+  dataset_size: 263683981
 - config_name: P
   features:
   - name: sentences
@@ -82,9 +92,9 @@ dataset_info:
   - name: validation
     num_bytes: 4776981
     num_examples: 2000
-  download_size: 120547669
+  download_size: 43825356
   dataset_size: 863707630
-- config_name: NE
+- config_name: V
   features:
   - name: sentences
     sequence: string
@@ -96,44 +106,75 @@ dataset_info:
     sequence: string
   splits:
   - name: train
-    num_bytes: 253551931
-    num_examples: 108719
+    num_bytes: 252177649
+    num_examples: 105825
   - name: test
-    num_bytes: 5707734
+    num_bytes: 5806625
     num_examples: 2500
   - name: validation
-    num_bytes: 4424316
+    num_bytes: 4556425
     num_examples: 2000
-  download_size: 120547669
-  dataset_size: 263683981
-- config_name: CN
+  download_size: 29992082
+  dataset_size: 262540699
+- config_name: raw
   features:
-  - name: sentences
-    sequence: string
-  - name: question
+  - name: title
     dtype: string
-  - name: answer
+  - name: content
     dtype: string
-  - name: options
-    sequence: string
   splits:
   - name: train
-    num_bytes: 301730151
-    num_examples: 120769
+    num_bytes: 25741580
+    num_examples: 98
   - name: test
-    num_bytes: 6138376
-    num_examples: 2500
+    num_bytes: 1528704
+    num_examples: 5
   - name: validation
-    num_bytes: 4737257
-    num_examples: 2000
-  download_size: 120547669
-  dataset_size: 312605784
-config_names:
-- CN
-- NE
-- P
-- V
-- raw
+    num_bytes: 1182657
+    num_examples: 5
+  download_size: 16350790
+  dataset_size: 28452941
+configs:
+- config_name: CN
+  data_files:
+  - split: train
+    path: CN/train-*
+  - split: test
+    path: CN/test-*
+  - split: validation
+    path: CN/validation-*
+- config_name: NE
+  data_files:
+  - split: train
+    path: NE/train-*
+  - split: test
+    path: NE/test-*
+  - split: validation
+    path: NE/validation-*
+- config_name: P
+  data_files:
+  - split: train
+    path: P/train-*
+  - split: test
+    path: P/test-*
+  - split: validation
+    path: P/validation-*
+- config_name: V
+  data_files:
+  - split: train
+    path: V/train-*
+  - split: test
+    path: V/test-*
+  - split: validation
+    path: V/validation-*
+- config_name: raw
+  data_files:
+  - split: train
+    path: raw/train-*
+  - split: test
+    path: raw/test-*
+  - split: validation
+    path: raw/validation-*
 ---
 
 # Dataset Card for CBT

V/test-00000-of-00001.parquet

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+size 2099626

V/train-00000-of-00001.parquet

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+size 26506525

V/validation-00000-of-00001.parquet

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+size 1385931

cbt.py

@@ -1,206 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Children's Book Test Dataset."""
-
-
-import datasets
-
-
-_CITATION = """\
-@misc{hill2016goldilocks,
-      title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
-      author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston},
-      year={2016},
-      eprint={1511.02301},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-
-_DESCRIPTION = """\
-The Children’s Book Test (CBT) is designed to measure directly
-how well language models can exploit wider linguistic context.
-The CBT is built from books that are freely available.
-"""
-
-_HOMEPAGE = "https://research.fb.com/downloads/babi/"
-
-_LICENSE = """GNU Free Documentation License v1.3"""
-
-ZIP_URL = "data/CBTest.tgz"
-dir = "CBTest/data/"
-paths = {
-    "raw": {"train": dir + "cbt_train.txt", "valid": dir + "cbt_valid.txt", "test": dir + "cbt_test.txt"},
-    "V": {
-        "train": dir + "cbtest_V_train.txt",
-        "valid": dir + "cbtest_V_valid_2000ex.txt",
-        "test": dir + "cbtest_V_test_2500ex.txt",
-    },
-    "P": {
-        "train": dir + "cbtest_P_train.txt",
-        "valid": dir + "cbtest_P_valid_2000ex.txt",
-        "test": dir + "cbtest_P_test_2500ex.txt",
-    },
-    "NE": {
-        "train": dir + "cbtest_NE_train.txt",
-        "valid": dir + "cbtest_NE_valid_2000ex.txt",
-        "test": dir + "cbtest_NE_test_2500ex.txt",
-    },
-    "CN": {
-        "train": dir + "cbtest_CN_train.txt",
-        "valid": dir + "cbtest_CN_valid_2000ex.txt",
-        "test": dir + "cbtest_CN_test_2500ex.txt",
-    },
-}
-
-
-class Cbt(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="raw", version=VERSION, description="This part of my dataset covers the raw CBT books"
-        ),
-        datasets.BuilderConfig(
-            name="V", version=VERSION, description="This part of my dataset covers the verb answer CBT dataset"
-        ),
-        datasets.BuilderConfig(
-            name="P", version=VERSION, description="This part of my dataset covers the preposition answer CBT dataset"
-        ),
-        datasets.BuilderConfig(
-            name="NE",
-            version=VERSION,
-            description="This part of my dataset covers the named entity answer CBT dataset",
-        ),
-        datasets.BuilderConfig(
-            name="CN", version=VERSION, description="This part of my dataset covers the common noun answer CBT dataset"
-        ),
-    ]
-
-    def _info(self):
-        if self.config.name in ["V", "P", "NE", "CN"]:
-            features = datasets.Features(
-                {
-                    "sentences": datasets.Sequence(datasets.Value("string")),  # There are 20 sentences
-                    "question": datasets.Value("string"),
-                    "answer": datasets.Value("string"),
-                    "options": datasets.Sequence(datasets.Value("string")),
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features({"title": datasets.Value("string"), "content": datasets.Value("string")})
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        my_urls = ZIP_URL  # Cannot download just one single type as it is a compressed file.
-        archive = dl_manager.download(my_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": paths[self.config.name]["train"], "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": paths[self.config.name]["test"], "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": paths[self.config.name]["valid"], "files": dl_manager.iter_archive(archive)},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Yields examples as (key, example) tuples."""
-        for path, f in files:
-            if path == filepath:
-                if self.config.name != "raw":
-                    sentences = []
-                    example_idx = 0
-                    for idx, line in enumerate(f):
-                        line = line.decode("utf-8")
-                        if line.strip() == "":
-                            continue
-
-                        elif line.split()[0] == "21":
-                            splitline = line.split("\t")  # question, answer options are tab separated
-                            question = splitline[0]
-                            answer = splitline[1]
-                            options = splitline[-1]
-                            question = question[2:].strip()  # The first two indices contain `21`.
-                            answer = answer.strip()
-                            options = options.strip().split("|")
-                            yield example_idx, {
-                                "sentences": sentences,
-                                "question": question,
-                                "options": options,
-                                "answer": answer,
-                            }
-
-                            sentences = []
-                            example_idx += 1
-                        else:
-                            if len(line.split()[0]) == 1:
-                                sentences.append(line[1:].strip())
-                            else:
-                                sentences.append(line[2:].strip())
-                                # Text might contain double spaces.
-                else:
-                    book_idx = 0
-                    book_sentences = []
-                    for idx, line in enumerate(f):
-                        line = line.decode("utf-8")
-                        if line[:12] == "_BOOK_TITLE_":
-                            if idx == 0:  # First line:
-                                title = line.split(":")[1].strip()
-                            else:
-                                yield book_idx, {
-                                    "title": title,
-                                    "content": "".join(book_sentences),
-                                }
-                                title = line.split(":")[1].strip()
-                                book_sentences = []
-                                book_idx += 1
-                        else:
-                            book_sentences.append(line)
-                    else:
-                        yield book_idx, {
-                            "title": title,
-                            "content": "".join(book_sentences),
-                        }
-                        book_sentences = []
-                        book_idx += 1
-                break

dataset_infos.json

@@ -1 +0,0 @@
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"patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 25741580, "num_examples": 98, "dataset_name": "cbt"}, "test": {"name": "test", "num_bytes": 1528704, "num_examples": 5, "dataset_name": "cbt"}, "validation": {"name": "validation", "num_bytes": 1182657, "num_examples": 5, "dataset_name": "cbt"}}, "download_checksums": {"data/CBTest.tgz": {"num_bytes": 120547669, "checksum": "932df0cadc1337b2a12b4c696b1041c1d1c6d4b6bd319874c6288f02e4a61e92"}}, "download_size": 120547669, "post_processing_size": null, "dataset_size": 28452941, "size_in_bytes": 149000610}, "V": {"description": "The Children\u2019s Book Test (CBT) is designed to measure directly\nhow well language models can exploit wider linguistic context.\nThe CBT is built from books that are freely available.\n", "citation": "@misc{hill2016goldilocks,\n      title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},\n      author={Felix Hill and Antoine Bordes and Sumit Chopra and 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