update with some datasets

mrqa.py

@@ -0,0 +1,237 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MRQA 2019 Shared task dataset."""
+
+import json
+
+import datasets
+
+_CITATION = """\
+@inproceedings{fisch2019mrqa,
+    title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
+    author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
+    booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP},
+    year={2019},
+}
+"""
+
+_DESCRIPTION = """\
+The MRQA 2019 Shared Task focuses on generalization in question answering.
+An effective question answering system should do more than merely
+interpolate from the training set to answer test examples drawn
+from the same distribution: it should also be able to extrapolate
+to out-of-distribution examples — a significantly harder challenge.
+The dataset is a collection of 18 existing QA dataset (carefully selected
+subset of them) and converted to the same format (SQuAD format). Among
+these 18 datasets, six datasets were made available for training,
+six datasets were made available for development, and the final six
+for testing. The dataset is released as part of the MRQA 2019 Shared Task.
+"""
+
+_HOMEPAGE = "https://mrqa.github.io/2019/shared.html"
+
+_LICENSE = "Unknwon"
+
+_URLs = {
+    # Train sub-datasets
+    "train+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz",
+    "train+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NewsQA.jsonl.gz",
+    "train+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/TriviaQA-web.jsonl.gz",
+    "train+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SearchQA.jsonl.gz",
+    "train+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/HotpotQA.jsonl.gz",
+    "train+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NaturalQuestionsShort.jsonl.gz",
+    # Validation sub-datasets
+    "validation+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SQuAD.jsonl.gz",
+    "validation+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NewsQA.jsonl.gz",
+    "validation+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TriviaQA-web.jsonl.gz",
+    "validation+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SearchQA.jsonl.gz",
+    "validation+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/HotpotQA.jsonl.gz",
+    "validation+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NaturalQuestionsShort.jsonl.gz",
+    # Test sub-datasets
+    "test+BioASQ": "http://participants-area.bioasq.org/MRQA2019/",  # BioASQ.jsonl.gz
+    "test+DROP": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DROP.jsonl.gz",
+    "test+DuoRC": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DuoRC.ParaphraseRC.jsonl.gz",
+    "test+RACE": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RACE.jsonl.gz",
+    "test+RelationExtraction": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RelationExtraction.jsonl.gz",
+    "test+TextbookQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TextbookQA.jsonl.gz",
+}
+
+
+class MRQAConfig(datasets.BuilderConfig):
+    """BuilderConfig for FS."""
+
+    def __init__(self, data_url, **kwargs):
+        """BuilderConfig for FS.
+        Args:
+          additional_features: `list[string]`, list of the features that will appear in the feature dict
+           additionally to the self.id_key, self.source_key and self.target_key. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MRQAConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+
+
+class MRQA(datasets.GeneratorBasedBuilder):
+    """MRQA 2019 Shared task dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        MRQAConfig(
+            name="newsqa",
+            data_url={"validation": _URLs["validation+NewsQA"],
+                      "train": _URLs["train+NewsQA"],
+                      "test": _URLs["validation+NewsQA"]}
+        ),
+        MRQAConfig(
+            name="natural_questions",
+            data_url={"validation": _URLs["validation+NaturalQuestions"],
+                      "train": _URLs["train+NaturalQuestions"],
+                      "test": _URLs["validation+NaturalQuestions"]}
+        ),
+        MRQAConfig(
+            name="hotpotqa",
+            data_url={"validation": _URLs["validation+HotpotQA"],
+                      "train": _URLs["train+HotpotQA"],
+                      "test": _URLs["validation+HotpotQA"]}
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            # Format is derived from https://github.com/mrqa/MRQA-Shared-Task-2019#mrqa-format
+            features=datasets.Features(
+                {
+                    "subset": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "context_tokens": datasets.Sequence(
+                        {
+                            "tokens": datasets.Value("string"),
+                            "offsets": datasets.Value("int32"),
+                        }
+                    ),
+                    "qid": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "question_tokens": datasets.Sequence(
+                        {
+                            "tokens": datasets.Value("string"),
+                            "offsets": datasets.Value("int32"),
+                        }
+                    ),
+                    "detected_answers": datasets.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "char_spans": datasets.Sequence(
+                                {
+                                    "start": datasets.Value("int32"),
+                                    "end": datasets.Value("int32"),
+                                }
+                            ),
+                            "token_spans": datasets.Sequence(
+                                {
+                                    "start": datasets.Value("int32"),
+                                    "end": datasets.Value("int32"),
+                                }
+                            ),
+                        }
+                    ),
+                    "answers": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(self.config.data_url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths_dict": data_dir,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths_dict": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths_dict": data_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepaths_dict, split):
+        """Yields examples."""
+        for source, filepath in filepaths_dict.items():
+            if source not in split:
+                continue
+            with open(filepath, encoding="utf-8") as f:
+                header = next(f)
+                subset = json.loads(header)["header"]["dataset"]
+
+                for row in f:
+                    paragraph = json.loads(row)
+                    context = paragraph["context"].strip()
+                    context_tokens = [{"tokens": t[0], "offsets": t[1]} for t in paragraph["context_tokens"]]
+                    for qa in paragraph["qas"]:
+                        qid = qa["qid"]
+                        question = qa["question"].strip()
+                        question_tokens = [{"tokens": t[0], "offsets": t[1]} for t in qa["question_tokens"]]
+                        detected_answers = []
+                        for detect_ans in qa["detected_answers"]:
+                            detected_answers.append(
+                                {
+                                    "text": detect_ans["text"].strip(),
+                                    "char_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["char_spans"]],
+                                    "token_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["token_spans"]],
+                                }
+                            )
+                        answers = qa["answers"]
+                        final_row = {
+                            "subset": subset,
+                            "context": context,
+                            "context_tokens": context_tokens,
+                            "qid": qid,
+                            "question": question,
+                            "question_tokens": question_tokens,
+                            "detected_answers": detected_answers,
+                            "answers": answers,
+                        }
+                        yield f"{source}_{qid}", final_row
+
+
+if __name__ == '__main__':
+    from datasets import load_dataset
+    ssfd_debug = load_dataset("/Users/yuvalkirstain/repos/mrqa", name="hotpotqa")
+    x = 5