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:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- fr-FR
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+- open-domain-qa
 paperswithcode_id: null
 ---
 
@@ -91,9 +110,9 @@ The data fields are the same among all splits.
 
 ### Data Splits
 
-|   name   |train|
-|----------|----:|
-|plain_text| 3835|
+| name       | train |
+| ---------- | ----: |
+| plain_text |  3835 |
 
 ## Dataset Creation
 

dataset_infos.json

@@ -1 +1 @@
-{"plain_text": {"description": "Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.\n", "citation": "@InProceedings{keraron-EtAl:2020:LREC,\n  author    = {Keraron, Rachel  and  Lancrenon, Guillaume  and  Bras, Mathilde  and  Allary, Fr\u00c3\u00a9d\u00c3\u00a9ric  and  Moyse, Gilles  and  Scialom, Thomas  and  Soriano-Morales, Edmundo-Pavel  and  Staiano, Jacopo},\n  title     = {Project PIAF: Building a Native French Question-Answering Dataset},\n  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},\n  month          = {May},\n  year           = {2020},\n  address        = {Marseille, France},\n  publisher      = {European Language Resources Association},\n  pages     = {5483--5492},\n  abstract  = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},\n  url       = {https://www.aclweb.org/anthology/2020.lrec-1.673}\n}\n", "homepage": "https://piaf.etalab.studio", "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": "piaf", "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": 3337705, "num_examples": 3835, "dataset_name": "piaf"}}, "download_checksums": {"https://github.com/etalab-ia/piaf-code/raw/master/piaf-v1.0.json": {"num_bytes": 1370384, "checksum": "008229ccefa0195d7e809d777d33149cab03433059c9477bdbadb4838a277cd2"}}, "download_size": 1370384, "dataset_size": 3337705, "size_in_bytes": 4708089}}
+{"plain_text": {"description": "Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.\n", "citation": "@InProceedings{keraron-EtAl:2020:LREC,\n  author    = {Keraron, Rachel  and  Lancrenon, Guillaume  and  Bras, Mathilde  and  Allary, Fr\u00e9d\u00e9ric  and  Moyse, Gilles  and  Scialom, Thomas  and  Soriano-Morales, Edmundo-Pavel  and  Staiano, Jacopo},\n  title     = {Project PIAF: Building a Native French Question-Answering Dataset},\n  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},\n  month          = {May},\n  year           = {2020},\n  address        = {Marseille, France},\n  publisher      = {European Language Resources Association},\n  pages     = {5483--5492},\n  abstract  = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},\n  url       = {https://www.aclweb.org/anthology/2020.lrec-1.673}\n}\n", "homepage": "https://piaf.etalab.studio", "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": "piaf", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3332905, "num_examples": 3835, "dataset_name": "piaf"}}, "download_checksums": {"https://github.com/etalab-ia/piaf-code/raw/master/piaf-v1.0.json": {"num_bytes": 1370384, "checksum": "008229ccefa0195d7e809d777d33149cab03433059c9477bdbadb4838a277cd2"}}, "download_size": 1370384, "post_processing_size": null, "dataset_size": 3332905, "size_in_bytes": 4703289}}

piaf.py

@@ -20,6 +20,7 @@
 import json
 
 import datasets
+from datasets.tasks import QuestionAnsweringExtractive
 
 
 logger = datasets.logging.get_logger(__name__)
@@ -93,6 +94,11 @@ class Piaf(datasets.GeneratorBasedBuilder):
             supervised_keys=None,
             homepage="https://piaf.etalab.studio",
             citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
         )
 
     def _split_generators(self, dl_manager):