Update parquet files

README.md

@@ -1,281 +0,0 @@
----
-language:
-- en
-paperswithcode_id: ms-marco
-pretty_name: Microsoft Machine Reading Comprehension Dataset
-dataset_info:
-- config_name: v1.1
-  features:
-  - name: answers
-    sequence: string
-  - name: passages
-    sequence:
-    - name: is_selected
-      dtype: int32
-    - name: passage_text
-      dtype: string
-    - name: url
-      dtype: string
-  - name: query
-    dtype: string
-  - name: query_id
-    dtype: int32
-  - name: query_type
-    dtype: string
-  - name: wellFormedAnswers
-    sequence: string
-  splits:
-  - name: validation
-    num_bytes: 42710107
-    num_examples: 10047
-  - name: train
-    num_bytes: 350884446
-    num_examples: 82326
-  - name: test
-    num_bytes: 41020711
-    num_examples: 9650
-  download_size: 168698008
-  dataset_size: 434615264
-- config_name: v2.1
-  features:
-  - name: answers
-    sequence: string
-  - name: passages
-    sequence:
-    - name: is_selected
-      dtype: int32
-    - name: passage_text
-      dtype: string
-    - name: url
-      dtype: string
-  - name: query
-    dtype: string
-  - name: query_id
-    dtype: int32
-  - name: query_type
-    dtype: string
-  - name: wellFormedAnswers
-    sequence: string
-  splits:
-  - name: validation
-    num_bytes: 414286005
-    num_examples: 101093
-  - name: train
-    num_bytes: 3466972085
-    num_examples: 808731
-  - name: test
-    num_bytes: 406197152
-    num_examples: 101092
-  download_size: 1384271865
-  dataset_size: 4287455242
----
-
-# Dataset Card for "ms_marco"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://microsoft.github.io/msmarco/](https://microsoft.github.io/msmarco/)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 1481.03 MB
-- **Size of the generated dataset:** 4503.32 MB
-- **Total amount of disk used:** 5984.34 MB
-
-### Dataset Summary
-
-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.
-
-version v1.1
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### v1.1
-
-- **Size of downloaded dataset files:** 160.88 MB
-- **Size of the generated dataset:** 414.48 MB
-- **Total amount of disk used:** 575.36 MB
-
-An example of 'train' looks as follows.
-```
-
-```
-
-#### v2.1
-
-- **Size of downloaded dataset files:** 1320.14 MB
-- **Size of the generated dataset:** 4088.84 MB
-- **Total amount of disk used:** 5408.98 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### v1.1
-- `answers`: a `list` of `string` features.
-- `passages`: a dictionary feature containing:
-  - `is_selected`: a `int32` feature.
-  - `passage_text`: a `string` feature.
-  - `url`: a `string` feature.
-- `query`: a `string` feature.
-- `query_id`: a `int32` feature.
-- `query_type`: a `string` feature.
-- `wellFormedAnswers`: a `list` of `string` features.
-
-#### v2.1
-- `answers`: a `list` of `string` features.
-- `passages`: a dictionary feature containing:
-  - `is_selected`: a `int32` feature.
-  - `passage_text`: a `string` feature.
-  - `url`: a `string` feature.
-- `query`: a `string` feature.
-- `query_id`: a `int32` feature.
-- `query_type`: a `string` feature.
-- `wellFormedAnswers`: a `list` of `string` features.
-
-### Data Splits
-
-|name|train |validation| test |
-|----|-----:|---------:|-----:|
-|v1.1| 82326|     10047|  9650|
-|v2.1|808731|    101093|101092|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-
-@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}
-}
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"v1.1": {"description": "\nStarting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.\n\nThe first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. \nSince then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, \nkeyphrase extraction dataset, crawling dataset, and a conversational search.\n\nThere have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking \nsubmissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions\n\nThis data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). \n\nThe original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.\n\nThe current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and \nis much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and \nbuilds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.\n\n\nversion v1.1", "citation": "\n@article{DBLP:journals/corr/NguyenRSGTMD16,\n  author    = {Tri Nguyen and\n               Mir Rosenberg and\n               Xia Song and\n               Jianfeng Gao and\n               Saurabh Tiwary and\n               Rangan Majumder and\n               Li Deng},\n  title     = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},\n  journal   = {CoRR},\n  volume    = {abs/1611.09268},\n  year      = {2016},\n  url       = {http://arxiv.org/abs/1611.09268},\n  archivePrefix = {arXiv},\n  eprint    = {1611.09268},\n  timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n}\n", "homepage": "https://microsoft.github.io/msmarco/", "license": "", "features": {"answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "passages": {"feature": {"is_selected": {"dtype": "int32", "id": null, "_type": "Value"}, "passage_text": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "query": {"dtype": "string", "id": null, "_type": "Value"}, "query_id": {"dtype": "int32", "id": null, "_type": "Value"}, "query_type": {"dtype": "string", "id": null, "_type": "Value"}, "wellFormedAnswers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "ms_marco", "config_name": "v1.1", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 42710107, "num_examples": 10047, "dataset_name": "ms_marco"}, "train": {"name": "train", "num_bytes": 350884446, "num_examples": 82326, "dataset_name": "ms_marco"}, "test": {"name": "test", "num_bytes": 41020711, "num_examples": 9650, "dataset_name": "ms_marco"}}, "download_checksums": {"https://msmarco.blob.core.windows.net/msmsarcov1/train_v1.1.json.gz": {"num_bytes": 110704491, "checksum": "2aaa60df3a758137f0bb7c01fe334858477eb46fa8665ea01588e553cda6aa9f"}, "https://msmarco.blob.core.windows.net/msmsarcov1/dev_v1.1.json.gz": {"num_bytes": 13493661, "checksum": "c70fcb1de78e635cf501264891a1a56d52e7f63e69623da7dd41d89a785d67ca"}, "https://msmarco.blob.core.windows.net/msmsarcov1/test_hidden_v1.1.json": {"num_bytes": 44499856, "checksum": "083aa4f4d86ba0cedb830ca9972eff69f73cbc32b1da26b8617205f0dedea757"}}, "download_size": 168698008, "dataset_size": 434615264, "size_in_bytes": 603313272}, "v2.1": {"description": "\nStarting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.\n\nThe first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. \nSince then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, \nkeyphrase extraction dataset, crawling dataset, and a conversational search.\n\nThere have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking \nsubmissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions\n\nThis data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). \n\nThe original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.\n\nThe current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and \nis much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and \nbuilds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.\n\n\nversion v2.1", "citation": "\n@article{DBLP:journals/corr/NguyenRSGTMD16,\n  author    = {Tri Nguyen and\n               Mir Rosenberg and\n               Xia Song and\n               Jianfeng Gao and\n               Saurabh Tiwary and\n               Rangan Majumder and\n               Li Deng},\n  title     = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},\n  journal   = {CoRR},\n  volume    = {abs/1611.09268},\n  year      = {2016},\n  url       = {http://arxiv.org/abs/1611.09268},\n  archivePrefix = {arXiv},\n  eprint    = {1611.09268},\n  timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n}\n", "homepage": "https://microsoft.github.io/msmarco/", "license": "", "features": {"answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "passages": {"feature": {"is_selected": {"dtype": "int32", "id": null, "_type": "Value"}, "passage_text": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "query": {"dtype": "string", "id": null, "_type": "Value"}, "query_id": {"dtype": "int32", "id": null, "_type": "Value"}, "query_type": {"dtype": "string", "id": null, "_type": "Value"}, "wellFormedAnswers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "ms_marco", "config_name": "v2.1", "version": {"version_str": "2.1.0", "description": "", "datasets_version_to_prepare": null, "major": 2, "minor": 1, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 414286005, "num_examples": 101093, "dataset_name": "ms_marco"}, "train": {"name": "train", "num_bytes": 3466972085, "num_examples": 808731, "dataset_name": "ms_marco"}, "test": {"name": "test", "num_bytes": 406197152, "num_examples": 101092, "dataset_name": "ms_marco"}}, "download_checksums": {"https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz": {"num_bytes": 1112116929, "checksum": "e91745411ca81e441a3bb75deb71ce000dc2fc31334085b7d499982f14218fe2"}, "https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz": {"num_bytes": 138303699, "checksum": "5b3c9c20d1808ee199a930941b0d96f79e397e9234f77a1496890b138df7cb3c"}, "https://msmarco.blob.core.windows.net/msmarco/eval_v2.1_public.json.gz": {"num_bytes": 133851237, "checksum": "05ac0e448450d507e7ff8e37f48a41cc2d015f5bd2c7974d2445f00a53625db6"}}, "download_size": 1384271865, "dataset_size": 4287455242, "size_in_bytes": 5671727107}}

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,
-                    }

v1.1/ms_marco-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdcdcd7cf6b3a38fdd6feac8a687d999c1728a51e33a0556f56405afb7fe3b47
+size 20484568

v1.1/ms_marco-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b0bf970fc8a5791c320069cb5e8015f80461353f56def41b6b2e781cf9ec7fb
+size 175452225

v1.1/ms_marco-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69d35f5f842801219405a6ce137981046d6f700aa37424f3ff6216272334b7e2
+size 21391357

v2.1/ms_marco-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5494a8ade1dc40c347d667b4a7ed57b3b28487d6d4f58ba3e917a08327c9eaa
+size 204396130

v2.1/ms_marco-train-00000-of-00007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf0b93163690f9518223ed5529a02e4a77a3e11ba035e5401d4fb1fc0792cb2f
+size 244987443

v2.1/ms_marco-train-00001-of-00007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97a3f62a7ded532a6ee2c63a9f533c3a751732ab2d130aedcc544971dd8b3fb0
+size 245540545

v2.1/ms_marco-train-00002-of-00007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c88ece22b49dc2d16812f9120ea464b2a23092bb14f234b182e2dad6869c6df9
+size 248686360

v2.1/ms_marco-train-00003-of-00007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeafc33d517c49ece0b748ce497191339b180206a82f6349e8c30ff33211153e
+size 249233580

v2.1/ms_marco-train-00004-of-00007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7210bd199c8bdeff550ef6337475ded4e4fa0754084772d93c0e417d988b6b56
+size 248805381

v2.1/ms_marco-train-00005-of-00007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:556cc5fc7b2faf61e91489c486657203124e287929ab9b70b1a5f432f6626e15
+size 244020051

v2.1/ms_marco-train-00006-of-00007.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9fd95af63e8b21d2b56f86860b8f64b63ba10f2f707c8fc1ad80c9dff73809e
+size 210489093

v2.1/ms_marco-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e88c8885a08342c163776c9e5b6576c78443d93618887e1b74d45e4be4fe0183
+size 209628786