Update files from the datasets library (from 1.8.0)

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

README.md

@@ -139,7 +139,9 @@ Data is provided in the same format as SQuAD 1.1. An example is shown below:
 - title: the title of the Wikipedia page from which the context is sourced
 - context: the context/passage
 - id: a string identifier for each question
-- answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text
+- answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text.
+
+Note that no answers are provided in the test set. Indeed, this dataset is part of the DynaBench benchmark, for which you can submit your predictions on the [website](https://dynabench.org/tasks/2#1).
 
 ### Data Splits
 
@@ -223,4 +225,4 @@ This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/lic
 ```
 ### Contributions
 
-Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.
+Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.

adversarial_qa.py

@@ -169,13 +169,14 @@ class AdversarialQA(datasets.GeneratorBasedBuilder):
         logger.info("generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
             squad = json.load(f)
+            id_ = 0
             for article in squad["data"]:
                 title = article.get("title", "").strip()
                 for paragraph in article["paragraphs"]:
                     context = paragraph["context"].strip()
                     for qa in paragraph["qas"]:
                         question = qa["question"].strip()
-                        id_ = qa["id"]
+                        qid = qa["id"]
 
                         answer_starts = [answer["answer_start"] for answer in qa["answers"]]
                         answers = [answer["text"].strip() for answer in qa["answers"]]
@@ -188,10 +189,12 @@ class AdversarialQA(datasets.GeneratorBasedBuilder):
                             "title": title,
                             "context": context,
                             "question": question,
-                            "id": id_,
+                            "id": qid,
                             "answers": {
                                 "answer_start": answer_starts,
                                 "text": answers,
                             },
                             "metadata": {"split": split, "model_in_the_loop": model_in_the_loop},
                         }
+
+                        id_ += 1