Add missing features to openbookqa dataset for additional config (#4278)

* Clean code

* Add missing features for 'additional' config

* Set main config as default

* Update metadata

* Update version number

* Update metadata

* Fix typo

* Update dummy data

Commit from https://github.com/huggingface/datasets/commit/86995fd86308e34f732cd3a3deb9a4e0cc8945cf

README.md

@@ -79,33 +79,51 @@ a subject.
 
 ### Data Instances
 
-#### additional
+#### main
 
 - **Size of downloaded dataset files:** 1.38 MB
 - **Size of the generated dataset:** 1.38 MB
 - **Total amount of disk used:** 2.75 MB
 
-An example of 'train' looks as follows.
+An example of 'train' looks as follows:
 ```
-
+{'id': '7-980',
+ 'question_stem': 'The sun is responsible for',
+ 'choices': {'text': ['puppies learning new tricks',
+   'children growing up and getting old',
+   'flowers wilting in a vase',
+   'plants sprouting, blooming and wilting'],
+  'label': ['A', 'B', 'C', 'D']},
+ 'answerKey': 'D'}
 ```
 
-#### main
+#### additional
 
 - **Size of downloaded dataset files:** 1.38 MB
 - **Size of the generated dataset:** 1.38 MB
 - **Total amount of disk used:** 2.75 MB
 
-An example of 'validation' looks as follows.
+An example of 'train' looks as follows:
 ```
-
+{'id': '7-980',
+ 'question_stem': 'The sun is responsible for',
+ 'choices': {'text': ['puppies learning new tricks',
+   'children growing up and getting old',
+   'flowers wilting in a vase',
+   'plants sprouting, blooming and wilting'],
+  'label': ['A', 'B', 'C', 'D']},
+ 'answerKey': 'D',
+ 'fact1': 'the sun is the source of energy for physical cycles on Earth',
+ 'humanScore': 1.0,
+ 'clarity': 2.0,
+ 'turkIdAnonymized': 'b356d338b7'}
 ```
 
 ### Data Fields
 
 The data fields are the same among all splits.
 
-#### additional
+#### main
 - `id`: a `string` feature.
 - `question_stem`: a `string` feature.
 - `choices`: a dictionary feature containing:
@@ -113,20 +131,24 @@ The data fields are the same among all splits.
   - `label`: a `string` feature.
 - `answerKey`: a `string` feature.
 
-#### main
+#### additional
 - `id`: a `string` feature.
 - `question_stem`: a `string` feature.
 - `choices`: a dictionary feature containing:
   - `text`: a `string` feature.
   - `label`: a `string` feature.
 - `answerKey`: a `string` feature.
+- `fact1` (`str`): oOriginating common knowledge core fact associated to the question.
+- `humanScore` (`float`): Human accuracy score.
+- `clarity` (`float`): Clarity score.
+- `turkIdAnonymized` (`str`): Anonymized crowd-worker ID.
 
 ### Data Splits
 
-|   name   |train|validation|test|
-|----------|----:|---------:|---:|
-|additional| 4957|       500| 500|
-|main      | 4957|       500| 500|
+| name       | train | validation | test |
+|------------|------:|-----------:|-----:|
+| main       |  4957 |        500 |  500 |
+| additional |  4957 |        500 |  500 |
 
 ## Dataset Creation
 

dataset_infos.json

@@ -1 +1 @@
-{"main": {"description": "OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic\n(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In\nparticular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge,\nand rich text comprehension.\nOpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of\na subject.\n", "citation": "@inproceedings{OpenBookQA2018,\n title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},\n author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},\n booktitle={EMNLP},\n year={2018}\n}\n", "homepage": "https://allenai.org/data/open-book-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question_stem": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "openbookqa", "config_name": "main", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 896034, "num_examples": 4957, "dataset_name": "openbookqa"}, "test": {"name": "test", "num_bytes": 91850, "num_examples": 500, "dataset_name": "openbookqa"}, "validation": {"name": "validation", "num_bytes": 95519, "num_examples": 500, "dataset_name": "openbookqa"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-website/data/OpenBookQA-V1-Sep2018.zip": {"num_bytes": 1446098, "checksum": "82368cf05df2e3b309c17d162e10b888b4d768fad6e171e0a041954c8553be46"}}, "download_size": 1446098, "post_processing_size": null, "dataset_size": 1083403, "size_in_bytes": 2529501}, "additional": {"description": "OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic\n(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In\nparticular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge,\nand rich text comprehension.\nOpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of\na subject.\n", "citation": "@inproceedings{OpenBookQA2018,\n title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},\n author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},\n booktitle={EMNLP},\n year={2018}\n}\n", "homepage": "https://allenai.org/data/open-book-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question_stem": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "openbookqa", "config_name": "additional", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 896034, "num_examples": 4957, "dataset_name": "openbookqa"}, "test": {"name": "test", "num_bytes": 91850, "num_examples": 500, "dataset_name": "openbookqa"}, "validation": {"name": "validation", "num_bytes": 95519, "num_examples": 500, "dataset_name": "openbookqa"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-website/data/OpenBookQA-V1-Sep2018.zip": {"num_bytes": 1446098, "checksum": "82368cf05df2e3b309c17d162e10b888b4d768fad6e171e0a041954c8553be46"}}, "download_size": 1446098, "post_processing_size": null, "dataset_size": 1083403, "size_in_bytes": 2529501}}
+{"main": {"description": "OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic\n(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In\nparticular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge,\nand rich text comprehension.\nOpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding\nof a subject.\n", "citation": "@inproceedings{OpenBookQA2018,\n title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},\n author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},\n booktitle={EMNLP},\n year={2018}\n}\n", "homepage": "https://allenai.org/data/open-book-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question_stem": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "openbookqa", "config_name": "main", "version": {"version_str": "1.0.1", "description": "", "major": 1, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 896034, "num_examples": 4957, "dataset_name": "openbookqa"}, "validation": {"name": "validation", "num_bytes": 95519, "num_examples": 500, "dataset_name": "openbookqa"}, "test": {"name": "test", "num_bytes": 91850, "num_examples": 500, "dataset_name": "openbookqa"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-website/data/OpenBookQA-V1-Sep2018.zip": {"num_bytes": 1446098, "checksum": "82368cf05df2e3b309c17d162e10b888b4d768fad6e171e0a041954c8553be46"}}, "download_size": 1446098, "post_processing_size": null, "dataset_size": 1083403, "size_in_bytes": 2529501}, "additional": {"description": "OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic\n(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In\nparticular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge,\nand rich text comprehension.\nOpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding\nof a subject.\n", "citation": "@inproceedings{OpenBookQA2018,\n title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},\n author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},\n booktitle={EMNLP},\n year={2018}\n}\n", "homepage": "https://allenai.org/data/open-book-qa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question_stem": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}, "fact1": {"dtype": "string", "id": null, "_type": "Value"}, "humanScore": {"dtype": "float32", "id": null, "_type": "Value"}, "clarity": {"dtype": "float32", "id": null, "_type": "Value"}, "turkIdAnonymized": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "openbookqa", "config_name": "additional", "version": {"version_str": "1.0.1", "description": "", "major": 1, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 1290473, "num_examples": 4957, "dataset_name": "openbookqa"}, "validation": {"name": "validation", "num_bytes": 136141, "num_examples": 500, "dataset_name": "openbookqa"}, "test": {"name": "test", "num_bytes": 130926, "num_examples": 500, "dataset_name": "openbookqa"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-website/data/OpenBookQA-V1-Sep2018.zip": {"num_bytes": 1446098, "checksum": "82368cf05df2e3b309c17d162e10b888b4d768fad6e171e0a041954c8553be46"}}, "download_size": 1446098, "post_processing_size": null, "dataset_size": 1557540, "size_in_bytes": 3003638}}

openbookqa.py

@@ -1,4 +1,4 @@
-"""TODO(openBookQA): Add a description here."""
+"""OpenBookQA dataset."""
 
 
 import json
@@ -8,7 +8,17 @@ import textwrap
 import datasets
 
 
-# TODO(openBookQA): BibTeX citation
+_HOMEPAGE = "https://allenai.org/data/open-book-qa"
+
+_DESCRIPTION = """\
+OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic
+(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In
+particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge,
+and rich text comprehension.
+OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding
+of a subject.
+"""
+
 _CITATION = """\
 @inproceedings{OpenBookQA2018,
  title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
@@ -18,39 +28,25 @@ _CITATION = """\
 }
 """
 
-# TODO(openBookQA):
-_DESCRIPTION = textwrap.dedent(
-    """\
-OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic
-(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In
-particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge,
-and rich text comprehension.
-OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of
-a subject.
-"""
-)
 _URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/OpenBookQA-V1-Sep2018.zip"
 
 
 class OpenbookqaConfig(datasets.BuilderConfig):
-    def __init__(self, data_dir, **kwargs):
+    def __init__(self, data_dir=None, filenames=None, version=datasets.Version("1.0.1", ""), **kwargs):
         """BuilderConfig for openBookQA dataset
 
         Args:
           data_dir: directory for the given dataset name
           **kwargs: keyword arguments forwarded to super.
         """
-
-        super().__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
+        super().__init__(version=version, **kwargs)
         self.data_dir = data_dir
+        self.filenames = filenames
 
 
 class Openbookqa(datasets.GeneratorBasedBuilder):
-    """TODO(openBookQA): Short description of my dataset."""
+    """OpenBookQA dataset."""
 
-    # TODO(openBookQA): Set up version.
-    VERSION = datasets.Version("0.1.0")
     BUILDER_CONFIGS = [
         OpenbookqaConfig(
             name="main",
@@ -65,6 +61,11 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
                 """
             ),
             data_dir="Main",
+            filenames={
+                "train": "train.jsonl",
+                "validation": "dev.jsonl",
+                "test": "test.jsonl",
+            },
         ),
         OpenbookqaConfig(
             name="additional",
@@ -76,18 +77,19 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
                 """
             ),
             data_dir="Additional",
+            filenames={
+                "train": "train_complete.jsonl",
+                "validation": "dev_complete.jsonl",
+                "test": "test_complete.jsonl",
+            },
         ),
     ]
+    DEFAULT_CONFIG_NAME = "main"
 
     def _info(self):
-        # TODO(openBookQA): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
+        if self.config.name == "main":
+            features = datasets.Features(
                 {
-                    # These are the features of your dataset like images, labels ...
                     "id": datasets.Value("string"),
                     "question_stem": datasets.Value("string"),
                     "choices": datasets.features.Sequence(
@@ -98,64 +100,51 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
                     ),
                     "answerKey": datasets.Value("string"),
                 }
-            ),
-            # 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="https://allenai.org/data/open-book-qa",
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "question_stem": datasets.Value("string"),
+                    "choices": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "label": datasets.Value("string"),
+                        }
+                    ),
+                    "answerKey": datasets.Value("string"),
+                    "fact1": datasets.Value("string"),
+                    "humanScore": datasets.Value("float"),
+                    "clarity": datasets.Value("float"),
+                    "turkIdAnonymized": datasets.Value("string"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(openBookQA): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
         dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "OpenBookQA-V1-Sep2018", "Data")
-        data_dir = os.path.join(data_dir, self.config.data_dir)
-        train_file = (
-            os.path.join(data_dir, "train.jsonl")
-            if self.config.name == "main"
-            else os.path.join(data_dir, "train_complete.jsonl")
-        )
-        test_file = (
-            os.path.join(data_dir, "test.jsonl")
-            if self.config.name == "main"
-            else os.path.join(data_dir, "test_complete.jsonl")
-        )
-        dev_file = (
-            os.path.join(data_dir, "dev.jsonl")
-            if self.config.name == "main"
-            else os.path.join(data_dir, "dev_complete.jsonl")
-        )
+        data_dir = os.path.join(dl_dir, "OpenBookQA-V1-Sep2018", "Data", self.config.data_dir)
+        splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": train_file},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": test_file},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": dev_file},
-            ),
+                name=split,
+                gen_kwargs={"filepath": os.path.join(data_dir, self.config.filenames[split])},
+            )
+            for split in splits
         ]
 
     def _generate_examples(self, filepath):
         """Yields examples."""
-        # TODO(openBookQA): Yields (key, example) tuples from the dataset
         with open(filepath, encoding="utf-8") as f:
-            for row in f:
+            for uid, row in enumerate(f):
                 data = json.loads(row)
-                yield data["id"], {
+                example = {
                     "id": data["id"],
                     "question_stem": data["question"]["stem"],
                     "choices": {
@@ -164,3 +153,7 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
                     },
                     "answerKey": data["answerKey"],
                 }
+                if self.config.name == "additional":
+                    for key in ["fact1", "humanScore", "clarity", "turkIdAnonymized"]:
+                        example[key] = data[key]
+                yield uid, example