add files

README.md

@@ -0,0 +1,147 @@
+---
+annotations_creators:
+- found
+language:
+- en
+language_creators:
+- found
+license: []
+multilinguality:
+- monolingual
+pretty_name: CausalQA
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+tags:
+- question-answering
+- english
+- causal
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@alamhanz](https://github.com/alamhanz) and [@andreaschandra](https://github.com/andreaschandra) for adding this dataset.

causalqa.py

@@ -0,0 +1,116 @@
+"""Causal QA : """
+import os
+import sys
+import json
+import csv
+import yaml
+
+import datasets
+
+class CausalqaConfig(datasets.BuilderConfig):
+    """BuilderConfig for causalqa."""
+
+    def __init__(
+        self,
+        data_features,
+        data_url,
+        citation,
+        **kwargs
+    ):
+        """BuilderConfig for GLUE.
+        Args:
+          data_features: `dict[string, string]`, map from the name of the feature
+            dict for each text field to the name of the column in the tsv file
+          data_url: `dict[string, string]`, url to download the zip file from
+          citation: `string`, citation for the data set
+          process_label: `Function[string, any]`, function  taking in the raw value
+            of the label and processing it to the form required by the label feature
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CausalqaConfig, self).__init__(**kwargs)
+        self.data_features = data_features
+        self.data_url = data_url
+        self.citation = citation
+
+def OneBuild(data_info,feat_meta):
+    main_name = [*data_info][0]
+    submain_name = data_info[main_name].keys()
+    all_config = []
+    for k in submain_name:
+        fm_temp = feat_meta[main_name][k]
+        one_data_info = data_info[main_name][k]
+        cqa_config = CausalqaConfig(
+          name="{}.{}".format(main_name,k),
+          description=one_data_info["description"],
+          version=datasets.Version(one_data_info["version"], ""),
+          data_features=fm_temp,
+          data_url=one_data_info["url_data"],
+          citation=one_data_info["citation"]
+        )
+        all_config.append(cqa_config)
+    return all_config
+
+print(os.listdir())
+
+_FILE_PATH = os.getcwd()
+_PATH_SOURCE = os.path.join(_FILE_PATH, 'source')
+_PATH_METADATA = os.path.join(_PATH_SOURCE, 'features_metadata.yaml')
+_FILE_URL = json.load(open(os.path.join(_PATH_SOURCE, 'dataset_info.json')))
+_CAUSALQA_DESCRIPTION = ''.join(open(os.path.join(_PATH_SOURCE, 'dataset_description.txt')).readlines())
+_HOMEPAGE = _FILE_URL['homepage']
+
+all_files = _FILE_URL['files']
+
+class CausalQA(datasets.GeneratorBasedBuilder):
+    """CausalQA: An QA causal type dataset."""
+    with open(_PATH_METADATA, "r") as stream:
+        try:
+            fmeta = yaml.safe_load(stream)
+        except yaml.YAMLError as exc:
+            print(exc)
+
+    BUILDER_CONFIGS = []
+    for f in all_files:
+        BUILDER_CONFIGS += (OneBuild(f, fmeta))
+
+    def _info(self):
+        self.features = {feat: datasets.Value(self.config.data_features[feat]) 
+                         for feat in self.config.data_features}
+        
+        return datasets.DatasetInfo(
+            description=_CAUSALQA_DESCRIPTION,
+            features=datasets.Features(self.features),
+            homepage=_HOMEPAGE
+        )
+
+    def _split_generators(self, dl_manager):
+        data_train = dl_manager.download(self.config.data_url['train'])
+        data_val = dl_manager.download(self.config.data_url['val'])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_train 
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_val ## keys (as parameters) is used during generate example
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        csv.field_size_limit(1000000000)
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file, delimiter=",")
+            next(csv_reader)
+
+            ## the yield depends on files features
+            for id_, row in enumerate(csv_reader):
+                existing_values = row
+                feature_names = [*self.features]
+                one_example_row = dict(zip(feature_names, existing_values))
+                yield id_, one_example_row

dataset_infos.json

@@ -0,0 +1 @@
+{"eli5.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "eli5.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1174541, "num_examples": 117929, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 130497, "num_examples": 13104, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=1-FKsZknoDE7bh0fKucs8nNu72IKFxfuo": {"num_bytes": 820757, "checksum": "92304a6f44fee943c29e67c0097dbe287e4a420c101fb865d4d8d6098299a8c2"}, "https://drive.google.com/uc?id=108bG1CJMaqANIqLvxthuQvsZro-5qwbX": {"num_bytes": 91188, "checksum": "37d29f228db3a35e871c2124fe0b854f5a399951cda0a877891e7180ee080884"}}, "download_size": 911945, "post_processing_size": null, "dataset_size": 1305038, "size_in_bytes": 2216983}, "eli5.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "eli5.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1174541, "num_examples": 117929, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 130497, "num_examples": 13104, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=11FfVASpa-Agie4AJfEPdjkR-QrvEO5Sd": {"num_bytes": 820757, "checksum": "92304a6f44fee943c29e67c0097dbe287e4a420c101fb865d4d8d6098299a8c2"}, "https://drive.google.com/uc?id=11XlFn79xGSGmPu-TU2SYariQgg2bby2h": {"num_bytes": 91188, "checksum": "37d29f228db3a35e871c2124fe0b854f5a399951cda0a877891e7180ee080884"}}, "download_size": 911945, "post_processing_size": null, "dataset_size": 1305038, "size_in_bytes": 2216983}, "gooaq.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "gooaq.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 95226834, "num_examples": 146253, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 20122, "num_examples": 33, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=10A8rPzMECn3s-qXSZRGKnlDMx5h6-U6k": {"num_bytes": 93129949, "checksum": "9cd916d9ab41e70df0f6feec2d5818dfe733209cfa4803e5cfb033ebbba0133c"}, "https://drive.google.com/uc?id=10PNaYwLxFBfjm2AMM65sp-kRs9rE-8_A": {"num_bytes": 19740, "checksum": "3c127cd582e52889b7a78dc7084333d646019054a39ca9d6849d2ea8fa156a6f"}}, "download_size": 93149689, "post_processing_size": null, "dataset_size": 95246956, "size_in_bytes": 188396645}, "gooaq.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "gooaq.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 85723336, "num_examples": 131657, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 9523620, "num_examples": 14629, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=10cZ6Kx7v_UHzHkvBgduoiJ7Ny3aeO-Ww": {"num_bytes": 83835750, "checksum": "1e72feb89d26e6fc2bac718115c4a8aca63f6f6278d75585952e61976ea9dd77"}, "https://drive.google.com/uc?id=11YdwCGTaw7jKk612tRc2T6NSYRUgmAjk": {"num_bytes": 9313939, "checksum": "0e48376bde7400059cc874aca3864e39909ad51a6dbb97ba6ff5e2d7b05ab331"}}, "download_size": 93149689, "post_processing_size": null, "dataset_size": 95246956, "size_in_bytes": 188396645}, "hotpotqa.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "hotpotqa.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4465930, "num_examples": 355, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 437034, "num_examples": 35, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=103bDQK53aT1wIFbJqe9B8Usi0_TCROBT": {"num_bytes": 4484332, "checksum": "0b954243ecbf904cc3ae0b7b85b922380f2febd6a802fb5ab04ac313198f1705"}, "https://drive.google.com/uc?id=1-ZSJNCjxdh5wEifkVVseieYYu45tVbEN": {"num_bytes": 438660, "checksum": "2f5151490204e71fcb63d17288c2907666125ca1f35601935d2e8a7101df100f"}}, "download_size": 4922992, "post_processing_size": null, "dataset_size": 4902964, "size_in_bytes": 9825956}, "hotpotqa.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "hotpotqa.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4393383, "num_examples": 351, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 509581, "num_examples": 39, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=11GYpcyT98XTomZhQxai5OLrTFoTGrfJJ": {"num_bytes": 4411273, "checksum": "6ba37d116a3bd64e88e63a613e7d74122ec749e8ee9195dc8c90ced03b1bf57c"}, "https://drive.google.com/uc?id=11B-cH_N8VIyLFCyM_l4dWfkOURXf4ky-": {"num_bytes": 511719, "checksum": "992d67501169f1624572d897eda080f4bb08df1321bba18e64f559473156a9e9"}}, "download_size": 4922992, "post_processing_size": null, "dataset_size": 4902964, "size_in_bytes": 9825956}, "msmarco.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "msmarco.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 169604379, "num_examples": 23011, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 17559831, "num_examples": 2558, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=1-jMNHG6rS9b6TnRZ6iNbjRLXRwr8Znb_": {"num_bytes": 169308630, "checksum": "6d08c5da8205fd0ea8313d0e9bcc032b88ee5c53ce9a96081659be57a5157d61"}, "https://drive.google.com/uc?id=1-BtYcEWwgaD0aI5hHCHFXCZOZu8I2e8o": {"num_bytes": 17527966, "checksum": "01cea9955ec48381b9933179b6174642f65be72148b286128a5c0bbe89e25005"}}, "download_size": 186836596, "post_processing_size": null, "dataset_size": 187164210, "size_in_bytes": 374000806}, "msmarco.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "msmarco.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 168417032, "num_examples": 23012, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 18827061, "num_examples": 2557, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=111RXRWuNk2CMhDbzY2gIudmcB70lsQSy": {"num_bytes": 168122339, "checksum": "6be361f3386c2e30ed59572b7a25bec3afafef65da9a19ded4c6efaa79f43f50"}, "https://drive.google.com/uc?id=11OLGeEkS6Wkv3q5ObNAinim5hqB5UU4D": {"num_bytes": 18794406, "checksum": "426c0716422d6298caeda64663ca29d0758e007d32b00849064003cdb07b40c2"}}, "download_size": 186916745, "post_processing_size": null, "dataset_size": 187244093, "size_in_bytes": 374160838}, "naturalquestions.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "naturalquestions.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 111644341, "num_examples": 1137, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 7074666, "num_examples": 71, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=1-M_G-V7p0XvGmw3JWtOSfeAlrGLMu8U3": {"num_bytes": 111709402, "checksum": "7f74571bec9b0f55c5c527d831e547e0f860e3c241a5b06e7a6de5148deecd03"}, "https://drive.google.com/uc?id=1-hjnE4TvEp76eznP14DHIsqYnitY0rNW": {"num_bytes": 7078462, "checksum": "90b0130b16bdd3e429cbc07b094270748327127f841538a98d8dda1ac83f6897"}}, "download_size": 118787864, "post_processing_size": null, "dataset_size": 118719007, "size_in_bytes": 237506871}, "naturalquestions.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "naturalquestions.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 107149045, "num_examples": 1087, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 0, "num_examples": 0, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=118ALG23_Ayi7qrAAdExJKLiN21Xy7VO5": {"num_bytes": 107204249, "checksum": "993fc8e988edcc5b85984578adc77c03e42699b1ce5244e19eb2918a480a5d5e"}, "https://drive.google.com/uc?id=11L8JW9llwDI-vg2LSXL4NmnHbOOHETBr": {"num_bytes": 1103, "checksum": "9010e5a841cf0acfb13facfaa2c3318bc8118020ec071d15de099eb9a628fd01"}}, "download_size": 107205352, "post_processing_size": null, "dataset_size": 107149045, "size_in_bytes": 214354397}, "newsqa.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "newsqa.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4671092, "num_examples": 623, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 253141, "num_examples": 29, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=1-hvqrR98PcajkorCvFvqz4fcuinZfgvr": {"num_bytes": 4680426, "checksum": "7a92c04b59de7fd565c6bcb9f70285638d9796a53fd7c7138df9016f22b78c6f"}, "https://drive.google.com/uc?id=1-oZbc9QFvuDfxzDhwotOYwvcLfFbCJQC": {"num_bytes": 253785, "checksum": "1ac723e5500a33b5803bc004217d97ea1b37c345666e9080d5a1926b4d2e2ef3"}}, "download_size": 4934211, "post_processing_size": null, "dataset_size": 4924233, "size_in_bytes": 9858444}, "newsqa.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "newsqa.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4422767, "num_examples": 586, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 513622, "num_examples": 66, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=11mraicFI6bb6KFcUl3unZxW0OSGg18UB": {"num_bytes": 4431727, "checksum": "dd3959b6f0d73c034d159bc6abd58bddd3eccd8262742c172662ff9f676725cb"}, "https://drive.google.com/uc?id=10rM5-BYr1mrSVSRFqgiFuQdYvKuCDzD1": {"num_bytes": 514773, "checksum": "5b99daa84e3a1cd33a27bae3b419961183286e981df2f96b150096388508a3ee"}}, "download_size": 4946500, "post_processing_size": null, "dataset_size": 4936389, "size_in_bytes": 9882889}, "paq.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "paq.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1009499834, "num_examples": 692645, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 112181249, "num_examples": 76961, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=10PrKt6kEgq07SNss5rZizRcqQxcxS84X": {"num_bytes": 1002956199, "checksum": "bc5aa81e12bb689b47442ba65ad8be41f99b8aed6a88a1cdf7addd11d3ec652a"}, "https://drive.google.com/uc?id=1-kuu0RihKcve-EGFtwjYz8jOdN6rXbcM": {"num_bytes": 111450221, "checksum": "3be6746245b3479a3a4e0f1144ce5bfb09e5dad1976e9996e9a22ba38cf11955"}}, "download_size": 1114406420, "post_processing_size": null, "dataset_size": 1121681083, "size_in_bytes": 2236087503}, "paq.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "paq.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1009499834, "num_examples": 692645, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 112181249, "num_examples": 76961, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=11W8G2mmQ78LBwet5GFvwvNu5hfPWOblN": {"num_bytes": 1002956199, "checksum": "bc5aa81e12bb689b47442ba65ad8be41f99b8aed6a88a1cdf7addd11d3ec652a"}, "https://drive.google.com/uc?id=10iItHXCfQ9wIsFmDUIMjdXbOSapRCAwj": {"num_bytes": 111450221, "checksum": "3be6746245b3479a3a4e0f1144ce5bfb09e5dad1976e9996e9a22ba38cf11955"}}, "download_size": 1114406420, "post_processing_size": null, "dataset_size": 1121681083, "size_in_bytes": 2236087503}, "searchqa.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "searchqa.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 31403453, "num_examples": 663, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 5578556, "num_examples": 117, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=1-AOYSVQf4GI7UnXcZ2EYbDXszHetjdrS": {"num_bytes": 31416040, "checksum": "f615faee688b858cf08a30b31032a64b8df7ff3cca042b0b3bbbefdbd35fb1de"}, "https://drive.google.com/uc?id=1-ZCCflByWZ3sBE_Hxirhfy9KQQ8d2ABN": {"num_bytes": 5581361, "checksum": "804944d505f0940060703b75b103321f8338cddb7bb0c782151cdede1d4896d8"}}, "download_size": 36997401, "post_processing_size": null, "dataset_size": 36982009, "size_in_bytes": 73979410}, "searchqa.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "searchqa.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33071979, "num_examples": 702, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 3910030, "num_examples": 78, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=11ivqoK_aVjK6RpaT_QWjqJ3V9VMvw190": {"num_bytes": 33085689, "checksum": "d3a679a077bcc12c7e1f12f3504d4a5ec194d14ba20ec37c4db068ea536f6192"}, "https://drive.google.com/uc?id=11Uvh0s17N7hvwfPF75x0Ko6xdccfsZcl": {"num_bytes": 3911712, "checksum": "1cdc105f2d926210e70df5dadcf6457925cee057a9c06e13142cc3ef0d4b3203"}}, "download_size": 36997401, "post_processing_size": null, "dataset_size": 36982009, "size_in_bytes": 73979410}, "squad2.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "squad2.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5461054, "num_examples": 2957, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 549784, "num_examples": 252, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=10Akh_VNH5Kvp0BiKbq9irA-u5zgdoxPy": {"num_bytes": 5421114, "checksum": "8369ea80f63e550153051e63173ba0ecc5a9409e02e5b06839af483620191633"}, "https://drive.google.com/uc?id=10QszRRFigIz_bAWuhOkn3r3dngHLpDSy": {"num_bytes": 546425, "checksum": "ae2fa26d97f826c8496765c06767a71d5141c47860b2fc9f9b6df70cd288c807"}}, "download_size": 5967539, "post_processing_size": null, "dataset_size": 6010838, "size_in_bytes": 11978377}, "squad2.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "squad2.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5374088, "num_examples": 2888, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 589922, "num_examples": 321, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=110xR1sye2Qx-QrrOPb3IDLhkjLCqG1Zy": {"num_bytes": 5335047, "checksum": "f0f9202725d14c2ad867f0e7767112e8e7f059ece2de0b5fbeaae8dc5f9ff804"}, "https://drive.google.com/uc?id=1144-Zt5-b8nFZOgUXbnk7l-RLSHTnJmN": {"num_bytes": 585542, "checksum": "b4ed07a2def1a3ea6d482b52f5815701a5651f41cdc0a0306b08e2ec5bac58ad"}}, "download_size": 5920589, "post_processing_size": null, "dataset_size": 5964010, "size_in_bytes": 11884599}, "triviaqa.original-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "triviaqa.original-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11278937, "num_examples": 637, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 1229922, "num_examples": 66, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=1-lb4JylW7olUzbLJBaBqNK-HAcxulxEI": {"num_bytes": 11279573, "checksum": "8ebce60165aabfee8b3faab4999da9e06c615d1d46e10c4f5368069b11ecbc02"}, "https://drive.google.com/uc?id=1-T0LHqgSvKyIx6YehQ-TrBOhJqygV-Si": {"num_bytes": 1230213, "checksum": "1fd4524f83484d90275a945ecbcacbcca089db0d79bb534df1232b7ac3d5f70e"}}, "download_size": 12509786, "post_processing_size": null, "dataset_size": 12508859, "size_in_bytes": 25018645}, "triviaqa.random-split": {"description": "Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Bl\u00fcbaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_processed": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "context_processed": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "answer_processed": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "causalqa", "config_name": "triviaqa.random-split", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11358699, "num_examples": 632, "dataset_name": "causalqa"}, "validation": {"name": "validation", "num_bytes": 1268034, "num_examples": 71, "dataset_name": "causalqa"}}, "download_checksums": {"https://drive.google.com/uc?id=11-y9PSfAAP8-L8PtBd_51RaA-5MjsvPH": {"num_bytes": 11359562, "checksum": "27067cb12e15f7177c83bf5eebe666890d50bd24c629101a3e89fba24576c023"}, "https://drive.google.com/uc?id=11WzPoeBLWbfMyR8xozfl-xMOSqevIIrs": {"num_bytes": 1268294, "checksum": "bae89fd42b101dca27b03dc354f4c34ed78fbccd7e2640f2e65d4e01fd0f16cd"}}, "download_size": 12627856, "post_processing_size": null, "dataset_size": 12626733, "size_in_bytes": 25254589}}

source/dataset_description.txt

@@ -0,0 +1 @@
+Causal Question Answering Dataset is machine reading comprehension dataset from 10 QA datasets that are filtered using regex to get causal question. The dataset is from a paper titled CausalQA: A Benchmark for Causal Question Answering. 2022. Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast. In COLING.

source/dataset_info.json

@@ -0,0 +1,225 @@
+{
+    "homepage": "https://github.com/jakartaresearch",
+    "files": [
+        {
+            "eli5": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=1-FKsZknoDE7bh0fKucs8nNu72IKFxfuo",
+                        "val": "https://drive.google.com/uc?id=108bG1CJMaqANIqLvxthuQvsZro-5qwbX"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=11FfVASpa-Agie4AJfEPdjkR-QrvEO5Sd",
+                        "val": "https://drive.google.com/uc?id=11XlFn79xGSGmPu-TU2SYariQgg2bby2h"
+                    }
+                }
+            }
+        },
+        {
+            "gooaq": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=10A8rPzMECn3s-qXSZRGKnlDMx5h6-U6k",
+                        "val": "https://drive.google.com/uc?id=10PNaYwLxFBfjm2AMM65sp-kRs9rE-8_A"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=10cZ6Kx7v_UHzHkvBgduoiJ7Ny3aeO-Ww",
+                        "val": "https://drive.google.com/uc?id=11YdwCGTaw7jKk612tRc2T6NSYRUgmAjk"
+                    }
+                }
+            }
+        },
+        {
+            "hotpotqa": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=103bDQK53aT1wIFbJqe9B8Usi0_TCROBT",
+                        "val": "https://drive.google.com/uc?id=1-ZSJNCjxdh5wEifkVVseieYYu45tVbEN"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=11GYpcyT98XTomZhQxai5OLrTFoTGrfJJ",
+                        "val": "https://drive.google.com/uc?id=11B-cH_N8VIyLFCyM_l4dWfkOURXf4ky-"
+                    }
+                }
+            }
+        },
+        {
+            "msmarco": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=1-jMNHG6rS9b6TnRZ6iNbjRLXRwr8Znb_",
+                        "val": "https://drive.google.com/uc?id=1-BtYcEWwgaD0aI5hHCHFXCZOZu8I2e8o"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=111RXRWuNk2CMhDbzY2gIudmcB70lsQSy",
+                        "val": "https://drive.google.com/uc?id=11OLGeEkS6Wkv3q5ObNAinim5hqB5UU4D"
+                    }
+                }
+            }
+        },
+        {
+            "naturalquestions": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=1-M_G-V7p0XvGmw3JWtOSfeAlrGLMu8U3",
+                        "val": "https://drive.google.com/uc?id=1-hjnE4TvEp76eznP14DHIsqYnitY0rNW"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=118ALG23_Ayi7qrAAdExJKLiN21Xy7VO5",
+                        "val": "https://drive.google.com/uc?id=11L8JW9llwDI-vg2LSXL4NmnHbOOHETBr"
+                    }
+                }
+            }
+        },
+        {
+            "newsqa": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=1-hvqrR98PcajkorCvFvqz4fcuinZfgvr",
+                        "val": "https://drive.google.com/uc?id=1-oZbc9QFvuDfxzDhwotOYwvcLfFbCJQC"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=11mraicFI6bb6KFcUl3unZxW0OSGg18UB",
+                        "val": "https://drive.google.com/uc?id=10rM5-BYr1mrSVSRFqgiFuQdYvKuCDzD1"
+                    }
+                }
+            }
+        },
+        {
+            "paq": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=10PrKt6kEgq07SNss5rZizRcqQxcxS84X",
+                        "val": "https://drive.google.com/uc?id=1-kuu0RihKcve-EGFtwjYz8jOdN6rXbcM"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=11W8G2mmQ78LBwet5GFvwvNu5hfPWOblN",
+                        "val": "https://drive.google.com/uc?id=10iItHXCfQ9wIsFmDUIMjdXbOSapRCAwj"
+                    }
+                }
+            }
+        },
+        {
+            "searchqa": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=1-AOYSVQf4GI7UnXcZ2EYbDXszHetjdrS",
+                        "val": "https://drive.google.com/uc?id=1-ZCCflByWZ3sBE_Hxirhfy9KQQ8d2ABN"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=11ivqoK_aVjK6RpaT_QWjqJ3V9VMvw190",
+                        "val": "https://drive.google.com/uc?id=11Uvh0s17N7hvwfPF75x0Ko6xdccfsZcl"
+                    }
+                }
+            }
+        },
+        {
+            "squad2": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=10Akh_VNH5Kvp0BiKbq9irA-u5zgdoxPy",
+                        "val": "https://drive.google.com/uc?id=10QszRRFigIz_bAWuhOkn3r3dngHLpDSy"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=110xR1sye2Qx-QrrOPb3IDLhkjLCqG1Zy",
+                        "val": "https://drive.google.com/uc?id=1144-Zt5-b8nFZOgUXbnk7l-RLSHTnJmN"
+                    }
+                }
+            }
+        },
+        {
+            "triviaqa": {
+                "original-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=1-lb4JylW7olUzbLJBaBqNK-HAcxulxEI",
+                        "val": "https://drive.google.com/uc?id=1-T0LHqgSvKyIx6YehQ-TrBOhJqygV-Si"
+                    }
+                },
+                "random-split": {
+                    "description": "",
+                    "version": "1.0.0",
+                    "citation": "",
+                    "url_data": {
+                        "train": "https://drive.google.com/uc?id=11-y9PSfAAP8-L8PtBd_51RaA-5MjsvPH",
+                        "val": "https://drive.google.com/uc?id=11WzPoeBLWbfMyR8xozfl-xMOSqevIIrs"
+                    }
+                }
+            }
+        }
+    ]
+}

source/features_metadata.yaml

@@ -0,0 +1,158 @@
+eli5:
+  original-split:
+    id: string
+  random-split:
+    id: string
+gooaq:
+  original-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+hotpotqa:
+  original-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+msmarco:
+  original-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+naturalquestions:
+  original-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+newsqa:
+  original-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+paq:
+  original-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+searchqa:
+  original-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: int64
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+squad2:
+  original-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+triviaqa:
+  original-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string
+  random-split:
+    id: string
+    question: string
+    question_processed: string
+    context: string
+    context_processed: string
+    answer: string
+    answer_processed: string