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 +1,23 @@
 ---
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+languages:
+- it-IT
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- unknown
+source_datasets:
+- extended|squad
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+- extractive-qa
 paperswithcode_id: squad-it
 ---
 
@@ -91,9 +110,9 @@ The data fields are the same among all splits.
 
 ### Data Splits
 
-| name  |train|test|
-|-------|----:|---:|
-|default|54159|7609|
+| name    | train | test |
+| ------- | ----: | ---: |
+| default | 54159 | 7609 |
 
 ## Dataset Creation
 

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset \ninto Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.\n The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is \n split into training and test sets to support the replicability of the benchmarking of QA systems:\n", "citation": "@InProceedings{10.1007/978-3-030-03840-3_29,\n\tauthor=\"Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto\",\n\teditor=\"Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo\",\n\ttitle=\"Neural Learning for Question Answering in Italian\",\n\tbooktitle=\"AI*IA 2018 -- Advances in Artificial Intelligence\",\n\tyear=\"2018\",\n\tpublisher=\"Springer International Publishing\",\n\taddress=\"Cham\",\n\tpages=\"389--402\",\n\tisbn=\"978-3-030-03840-3\"\n}\n", "homepage": "https://github.com/crux82/squad-it", "license": "", "features": {"id": {"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": "squad_it", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 7870374, "num_examples": 7609, "dataset_name": "squad_it"}, "train": {"name": "train", "num_bytes": 50925634, "num_examples": 54159, "dataset_name": "squad_it"}}, "download_checksums": {"https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz": {"num_bytes": 7725286, "checksum": "75d4d2832961f7a0f76a43d7e919e56a880ccc55de434ec90ae82cd67bec5d25"}, "https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz": {"num_bytes": 1051245, "checksum": "25986c617cc7d58e82e916755b8a5684e5efae69835332858a6534a304cd293c"}}, "download_size": 8776531, "dataset_size": 58796008, "size_in_bytes": 67572539}}
+{"default": {"description": "SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset\ninto Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.\n The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is\n split into training and test sets to support the replicability of the benchmarking of QA systems:\n", "citation": "@InProceedings{10.1007/978-3-030-03840-3_29,\n    author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},\n    editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo\",\n    title={Neural Learning for Question Answering in Italian},\n    booktitle={AI*IA 2018 -- Advances in Artificial Intelligence},\n    year={2018},\n    publisher={Springer International Publishing},\n    address={Cham},\n    pages={389--402},\n    isbn={978-3-030-03840-3}\n}\n", "homepage": "https://github.com/crux82/squad-it", "license": "", "features": {"id": {"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": "squad_it", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 50864824, "num_examples": 54159, "dataset_name": "squad_it"}, "test": {"name": "test", "num_bytes": 7858336, "num_examples": 7609, "dataset_name": "squad_it"}}, "download_checksums": {"https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz": {"num_bytes": 7725286, "checksum": "75d4d2832961f7a0f76a43d7e919e56a880ccc55de434ec90ae82cd67bec5d25"}, "https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz": {"num_bytes": 1051245, "checksum": "25986c617cc7d58e82e916755b8a5684e5efae69835332858a6534a304cd293c"}}, "download_size": 8776531, "post_processing_size": null, "dataset_size": 58723160, "size_in_bytes": 67499691}}

squad_it.py

@@ -4,6 +4,7 @@
 import json
 
 import datasets
+from datasets.tasks import QuestionAnsweringExtractive
 
 
 # TODO(squad_it): BibTeX citation
@@ -69,6 +70,11 @@ class SquadIt(datasets.GeneratorBasedBuilder):
             # Homepage of the dataset for documentation
             homepage="https://github.com/crux82/squad-it",
             citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
         )
 
     def _split_generators(self, dl_manager):