Update files from the datasets library (from 1.9.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.9.0

README.md

@@ -1,6 +1,24 @@
 ---
+pretty_name: DROP
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
 languages:
 - en
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+- abstractive-qa
 paperswithcode_id: drop
 ---
 

drop.py

@@ -7,7 +7,6 @@ import os
 import datasets
 
 
-# TODO(drop): BibTeX citation
 _CITATION = """\
 @inproceedings{Dua2019DROP,
   author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
@@ -17,7 +16,6 @@ _CITATION = """\
 }
 """
 
-# TODO(drop):
 _DESCRIPTION = """\
 DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
 . DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
@@ -25,7 +23,7 @@ question, perhaps to multiple input positions, and perform discrete operations o
  sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
  necessary for prior datasets.
 """
-_URl = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
+_URL = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
 
 
 class AnswerParsingError(Exception):
@@ -102,7 +100,7 @@ class Drop(datasets.GeneratorBasedBuilder):
         # TODO(drop): 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)
+        dl_dir = dl_manager.download_and_extract(_URL)
         data_dir = os.path.join(dl_dir, "drop_dataset")
         return [
             datasets.SplitGenerator(
@@ -122,6 +120,7 @@ class Drop(datasets.GeneratorBasedBuilder):
         # TODO(drop): Yields (key, example) tuples from the dataset
         with open(filepath, mode="r", encoding="utf-8") as f:
             data = json.load(f)
+            id_ = 0
             for i, (section_id, section) in enumerate(data.items()):
                 for j, qa in enumerate(section["qa_pairs"]):
 
@@ -139,7 +138,8 @@ class Drop(datasets.GeneratorBasedBuilder):
 
                     try:
                         example["answers_spans"] = self.build_answers(answers)
-                        yield example["query_id"], example
+                        yield id_, example
+                        id_ += 1
                     except AnswerParsingError:
                         # This is expected for 9 examples of train
                         # and 1 of validation.