Update files from the datasets library (from 1.8.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

README.md

@@ -1,4 +1,22 @@
 ---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- ar-SA
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
 paperswithcode_id: arcd
 ---
 
@@ -90,9 +108,9 @@ The data fields are the same among all splits.
 
 ### Data Splits
 
-|   name   |train|validation|
-|----------|----:|---------:|
-|plain_text|  693|       702|
+| name       | train | validation |
+| ---------- | ----: | ---------: |
+| plain_text |   693 |        702 |
 
 ## Dataset Creation
 

arcd.py

@@ -4,6 +4,7 @@
 import json
 
 import datasets
+from datasets.tasks import QuestionAnsweringExtractive
 
 
 logger = datasets.logging.get_logger(__name__)
@@ -79,6 +80,11 @@ class Arcd(datasets.GeneratorBasedBuilder):
             supervised_keys=None,
             homepage="https://github.com/husseinmozannar/SOQAL/tree/master/data",
             citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
         )
 
     def _split_generators(self, dl_manager):

dataset_infos.json

@@ -1 +1 @@
-{"plain_text": {"description": " Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions      posed by crowdworkers on Wikipedia articles.\n", "citation": "@inproceedings{mozannar-etal-2019-neural,\n    title = \"Neural {A}rabic Question Answering\",\n    author = \"Mozannar, Hussein  and\n      Maamary, Elie  and\n      El Hajal, Karl  and\n      Hajj, Hazem\",\n    booktitle = \"Proceedings of the Fourth Arabic Natural Language Processing Workshop\",\n    month = aug,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W19-4612\",\n    doi = \"10.18653/v1/W19-4612\",\n    pages = \"108--118\",\n    abstract = \"This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.\",\n}\n", "homepage": "https://github.com/husseinmozannar/SOQAL/tree/master/data", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "arcd", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 811934, "num_examples": 693, "dataset_name": "arcd"}, "validation": {"name": "validation", "num_bytes": 886528, "num_examples": 702, "dataset_name": "arcd"}}, "download_checksums": {"https://raw.githubusercontent.com/husseinmozannar/SOQAL/master/data/arcd-train.json": {"num_bytes": 939840, "checksum": "6a973fda9f0b066e0547a85a3396e7294fa917e24b6efd7ce430769033a6ce15"}, "https://raw.githubusercontent.com/husseinmozannar/SOQAL/master/data/arcd-test.json": {"num_bytes": 1002559, "checksum": "b4ba4fb4227841bbce71e01b3eaecb33e9f17a08cde1ec91e5bc335da2c75135"}}, "download_size": 1942399, "dataset_size": 1698462, "size_in_bytes": 3640861}}
+{"plain_text": {"description": " Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions      posed by crowdworkers on Wikipedia articles.\n", "citation": "@inproceedings{mozannar-etal-2019-neural,\n    title = {Neural {A}rabic Question Answering},\n    author = {Mozannar, Hussein  and Maamary, Elie  and El Hajal, Karl  and Hajj, Hazem},\n    booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Workshop},\n    month = {aug},\n    year = {2019},\n    address = {Florence, Italy},\n    publisher = {Association for Computational Linguistics},\n    url = {https://www.aclweb.org/anthology/W19-4612},\n    doi = {10.18653/v1/W19-4612},\n    pages = {108--118},\n    abstract = {This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.}\n}\n", "homepage": "https://github.com/husseinmozannar/SOQAL/tree/master/data", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "arcd", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 811064, "num_examples": 693, "dataset_name": "arcd"}, "validation": {"name": "validation", "num_bytes": 885648, "num_examples": 702, "dataset_name": "arcd"}}, "download_checksums": {"https://raw.githubusercontent.com/husseinmozannar/SOQAL/master/data/arcd-train.json": {"num_bytes": 939840, "checksum": "6a973fda9f0b066e0547a85a3396e7294fa917e24b6efd7ce430769033a6ce15"}, "https://raw.githubusercontent.com/husseinmozannar/SOQAL/master/data/arcd-test.json": {"num_bytes": 1002559, "checksum": "b4ba4fb4227841bbce71e01b3eaecb33e9f17a08cde1ec91e5bc335da2c75135"}}, "download_size": 1942399, "post_processing_size": null, "dataset_size": 1696712, "size_in_bytes": 3639111}}