Update files from the datasets library (from 1.5.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.5.0

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.\n. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a \nquestion, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or\n sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was \n necessary for prior datasets.\n", "citation": "@inproceedings{Dua2019DROP,\n  author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},\n  title={  {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},\n  booktitle={Proc. of NAACL},\n  year={2019}\n}\n", "homepage": "https://allennlp.org/drop", "license": "", "features": {"passage": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers_spans": {"feature": {"spans": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "drop", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 100119741, "num_examples": 77409, "dataset_name": "drop"}, "validation": {"name": "validation", "num_bytes": 10788180, "num_examples": 9536, "dataset_name": "drop"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip": {"num_bytes": 8308692, "checksum": "39d2278a29fd729de301b111a45f434c24834f40df8f4ff116d864589e3249d6"}}, "download_size": 8308692, "dataset_size": 110907921, "size_in_bytes": 119216613}}
+{"default": {"description": "DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.\n. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a\nquestion, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or\n sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was\n necessary for prior datasets.\n", "citation": "@inproceedings{Dua2019DROP,\n  author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},\n  title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},\n  booktitle={Proc. of NAACL},\n  year={2019}\n}\n", "homepage": "https://allennlp.org/drop", "license": "", "features": {"section_id": {"dtype": "string", "id": null, "_type": "Value"}, "query_id": {"dtype": "string", "id": null, "_type": "Value"}, "passage": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers_spans": {"feature": {"spans": {"dtype": "string", "id": null, "_type": "Value"}, "types": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "drop", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 105572762, "num_examples": 77400, "dataset_name": "drop"}, "validation": {"name": "validation", "num_bytes": 11737787, "num_examples": 9535, "dataset_name": "drop"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip": {"num_bytes": 8308692, "checksum": "39d2278a29fd729de301b111a45f434c24834f40df8f4ff116d864589e3249d6"}}, "download_size": 8308692, "post_processing_size": null, "dataset_size": 117310549, "size_in_bytes": 125619241}}

drop.py

@@ -29,6 +29,42 @@ question, perhaps to multiple input positions, and perform discrete operations o
 _URl = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
 
 
+class AnswerParsingError(Exception):
+    pass
+
+
+class DropDateObject:
+    """
+    Custom parser for date answers in DROP.
+    A date answer is a dict <date> with at least one of day|month|year.
+
+    Example: date == {
+        'day': '9',
+        'month': 'March',
+        'year': '2021'
+    }
+
+    This dict is parsed and flattend to '{day} {month} {year}', not including
+    blank values.
+
+    Example: str(DropDateObject(date)) == '9 March 2021'
+    """
+
+    def __init__(self, dict_date):
+        self.year = dict_date.get("year", "")
+        self.month = dict_date.get("month", "")
+        self.day = dict_date.get("day", "")
+
+    def __iter__(self):
+        yield from [self.day, self.month, self.year]
+
+    def __bool__(self):
+        return any(self)
+
+    def __repr__(self):
+        return " ".join(self).strip()
+
+
 class Drop(datasets.GeneratorBasedBuilder):
     """TODO(drop): Short description of my dataset."""
 
@@ -43,9 +79,13 @@ class Drop(datasets.GeneratorBasedBuilder):
             # datasets.features.FeatureConnectors
             features=datasets.Features(
                 {
+                    "section_id": datasets.Value("string"),
+                    "query_id": datasets.Value("string"),
                     "passage": datasets.Value("string"),
                     "question": datasets.Value("string"),
-                    "answers_spans": datasets.features.Sequence({"spans": datasets.Value("string")})
+                    "answers_spans": datasets.features.Sequence(
+                        {"spans": datasets.Value("string"), "types": datasets.Value("string")}
+                    )
                     # These are the features of your dataset like images, labels ...
                 }
             ),
@@ -69,28 +109,95 @@ class Drop(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_train.json")},
+                gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_train.json"), "split": "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_dev.json")},
+                gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_dev.json"), "split": "validation"},
             ),
         ]
 
-    def _generate_examples(self, filepath):
+    def _generate_examples(self, filepath, split):
         """Yields examples."""
         # TODO(drop): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
+        with open(filepath, mode="r", encoding="utf-8") as f:
             data = json.load(f)
-            for i, key in enumerate(data):
-                example = data[key]
-                qa_pairs = example["qa_pairs"]
-                for j, qa in enumerate(qa_pairs):
-                    question = qa["question"]
-                    answers = qa["answer"]["spans"]
-                    yield str(i) + "_" + str(j), {
-                        "passage": example["passage"],
-                        "question": question,
-                        "answers_spans": {"spans": answers},
+            for i, (section_id, section) in enumerate(data.items()):
+                for j, qa in enumerate(section["qa_pairs"]):
+
+                    example = {
+                        "section_id": section_id,
+                        "query_id": qa["query_id"],
+                        "passage": section["passage"],
+                        "question": qa["question"],
                     }
+
+                    if split == "train":
+                        answers = [qa["answer"]]
+                    else:
+                        answers = qa["validated_answers"]
+
+                    try:
+                        example["answers_spans"] = self.build_answers(answers)
+                        yield example["query_id"], example
+                    except AnswerParsingError:
+                        # This is expected for 9 examples of train
+                        # and 1 of validation.
+                        continue
+
+    @staticmethod
+    def _raise(message):
+        """
+        Raise a custom AnswerParsingError, to be sure to only catch our own
+        errors. Messages are irrelavant for this script, but are written to
+        ease understanding the code.
+        """
+        raise AnswerParsingError(message)
+
+    def build_answers(self, answers):
+
+        returned_answers = {
+            "spans": list(),
+            "types": list(),
+        }
+        for answer in answers:
+            date = DropDateObject(answer["date"])
+
+            if answer["number"] != "":
+                # sanity checks
+                if date:
+                    self._raise("This answer is both number and date!")
+                if len(answer["spans"]):
+                    self._raise("This answer is both number and text!")
+
+                returned_answers["spans"].append(answer["number"])
+                returned_answers["types"].append("number")
+
+            elif date:
+                # sanity check
+                if len(answer["spans"]):
+                    self._raise("This answer is both date and text!")
+
+                returned_answers["spans"].append(str(date))
+                returned_answers["types"].append("date")
+
+            # won't triger if len(answer['spans']) == 0
+            for span in answer["spans"]:
+                # sanity checks
+                if answer["number"] != "":
+                    self._raise("This answer is both text and number!")
+                if date:
+                    self._raise("This answer is both text and date!")
+
+                returned_answers["spans"].append(span)
+                returned_answers["types"].append("span")
+
+        # sanity check
+        _len = len(returned_answers["spans"])
+        if not _len:
+            self._raise("Empty answer.")
+        if any(len(l) != _len for _, l in returned_answers.items()):
+            self._raise("Something went wrong while parsing answer values/types")
+
+        return returned_answers