Delete loading script

ms_marco.py

@@ -1,204 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""MS MARCO dataset."""
-
-
-import json
-
-import datasets
-
-
-_CITATION = """
-@article{DBLP:journals/corr/NguyenRSGTMD16,
-  author    = {Tri Nguyen and
-               Mir Rosenberg and
-               Xia Song and
-               Jianfeng Gao and
-               Saurabh Tiwary and
-               Rangan Majumder and
-               Li Deng},
-  title     = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
-  journal   = {CoRR},
-  volume    = {abs/1611.09268},
-  year      = {2016},
-  url       = {http://arxiv.org/abs/1611.09268},
-  archivePrefix = {arXiv},
-  eprint    = {1611.09268},
-  timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-}
-"""
-
-_DESCRIPTION = """
-Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
-
-The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.
-Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset,
-keyphrase extraction dataset, crawling dataset, and a conversational search.
-
-There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking
-submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions
-
-This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1).
-
-The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.
-
-The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and
-is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and
-builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.
-
-"""
-_V2_URLS = {
-    "train": "https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz",
-    "dev": "https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz",
-    "test": "https://msmarco.blob.core.windows.net/msmarco/eval_v2.1_public.json.gz",
-}
-
-_V1_URLS = {
-    "train": "https://msmarco.blob.core.windows.net/msmsarcov1/train_v1.1.json.gz",
-    "dev": "https://msmarco.blob.core.windows.net/msmsarcov1/dev_v1.1.json.gz",
-    "test": "https://msmarco.blob.core.windows.net/msmsarcov1/test_hidden_v1.1.json",
-}
-
-
-class MsMarcoConfig(datasets.BuilderConfig):
-    """BuilderConfig for MS MARCO."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for MS MARCO
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(MsMarcoConfig, self).__init__(**kwargs)
-
-
-class MsMarco(datasets.GeneratorBasedBuilder):
-
-    BUILDER_CONFIGS = [
-        MsMarcoConfig(
-            name="v1.1",
-            description="""version v1.1""",
-            version=datasets.Version("1.1.0", ""),
-        ),
-        MsMarcoConfig(
-            name="v2.1",
-            description="""version v2.1""",
-            version=datasets.Version("2.1.0", ""),
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION + "\n" + self.config.description,
-            features=datasets.Features(
-                {
-                    "answers": datasets.features.Sequence(datasets.Value("string")),
-                    "passages": datasets.features.Sequence(
-                        {
-                            "is_selected": datasets.Value("int32"),
-                            "passage_text": datasets.Value("string"),
-                            "url": datasets.Value("string"),
-                        }
-                    ),
-                    "query": datasets.Value("string"),
-                    "query_id": datasets.Value("int32"),
-                    "query_type": datasets.Value("string"),
-                    "wellFormedAnswers": datasets.features.Sequence(datasets.Value("string")),
-                }
-            ),
-            homepage="https://microsoft.github.io/msmarco/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        if self.config.name == "v2.1":
-            dl_path = dl_manager.download_and_extract(_V2_URLS)
-        else:
-            dl_path = dl_manager.download_and_extract(_V1_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dl_path["dev"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": dl_path["train"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": dl_path["test"]},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            if self.config.name == "v2.1":
-                data = json.load(f)
-                questions = data["query"]
-                answers = data.get("answers", {})
-                passages = data["passages"]
-                query_ids = data["query_id"]
-                query_types = data["query_type"]
-                wellFormedAnswers = data.get("wellFormedAnswers", {})
-                for key in questions:
-
-                    is_selected = [passage.get("is_selected", -1) for passage in passages[key]]
-                    passage_text = [passage["passage_text"] for passage in passages[key]]
-                    urls = [passage["url"] for passage in passages[key]]
-                    question = questions[key]
-                    answer = answers.get(key, [])
-                    query_id = query_ids[key]
-                    query_type = query_types[key]
-                    wellFormedAnswer = wellFormedAnswers.get(key, [])
-                    if wellFormedAnswer == "[]":
-                        wellFormedAnswer = []
-                    yield query_id, {
-                        "answers": answer,
-                        "passages": {"is_selected": is_selected, "passage_text": passage_text, "url": urls},
-                        "query": question,
-                        "query_id": query_id,
-                        "query_type": query_type,
-                        "wellFormedAnswers": wellFormedAnswer,
-                    }
-            if self.config.name == "v1.1":
-                for row in f:
-                    data = json.loads(row)
-                    question = data["query"]
-                    answer = data.get("answers", [])
-                    passages = data["passages"]
-                    query_id = data["query_id"]
-                    query_type = data["query_type"]
-                    wellFormedAnswer = data.get("wellFormedAnswers", [])
-
-                    is_selected = [passage.get("is_selected", -1) for passage in passages]
-                    passage_text = [passage["passage_text"] for passage in passages]
-                    urls = [passage["url"] for passage in passages]
-                    if wellFormedAnswer == "[]":
-                        wellFormedAnswer = []
-                    yield query_id, {
-                        "answers": answer,
-                        "passages": {"is_selected": is_selected, "passage_text": passage_text, "url": urls},
-                        "query": question,
-                        "query_id": query_id,
-                        "query_type": query_type,
-                        "wellFormedAnswers": wellFormedAnswer,
-                    }