Convert dataset to Parquet (#1)

- Convert dataset to Parquet (72112ee8ef9e57ecede00ddfa000cde9de421ba6)
- Add light data files (788845f32591342633c60167a81b60d521790efb)
- Delete loading script (a202432ef5aa99a29d0304a530a93877e1ffcfac)
- Delete legacy dataset_infos.json (1681964e138ab7f6961e9114974f6761fcfd0466)

README.md

@@ -21,7 +21,7 @@ task_ids:
 paperswithcode_id: ambigqa
 pretty_name: 'AmbigQA: Answering Ambiguous Open-domain Questions'
 dataset_info:
-- config_name: light
+- config_name: full
   features:
   - name: id
     dtype: string
@@ -39,16 +39,32 @@ dataset_info:
         dtype: string
       - name: answer
         sequence: string
+  - name: viewed_doc_titles
+    sequence: string
+  - name: used_queries
+    sequence:
+    - name: query
+      dtype: string
+    - name: results
+      sequence:
+      - name: title
+        dtype: string
+      - name: snippet
+        dtype: string
+  - name: nq_answer
+    sequence: string
+  - name: nq_doc_title
+    dtype: string
   splits:
   - name: train
-    num_bytes: 2739732
+    num_bytes: 43538533
     num_examples: 10036
   - name: validation
-    num_bytes: 805808
+    num_bytes: 15383268
     num_examples: 2002
-  download_size: 19700900
-  dataset_size: 3545540
-- config_name: full
+  download_size: 30674462
+  dataset_size: 58921801
+- config_name: light
   features:
   - name: id
     dtype: string
@@ -66,31 +82,29 @@ dataset_info:
         dtype: string
       - name: answer
         sequence: string
-  - name: viewed_doc_titles
-    sequence: string
-  - name: used_queries
-    sequence:
-    - name: query
-      dtype: string
-    - name: results
-      sequence:
-      - name: title
-        dtype: string
-      - name: snippet
-        dtype: string
-  - name: nq_answer
-    sequence: string
-  - name: nq_doc_title
-    dtype: string
   splits:
   - name: train
-    num_bytes: 43538733
+    num_bytes: 2739628
     num_examples: 10036
   - name: validation
-    num_bytes: 15383368
+    num_bytes: 805756
     num_examples: 2002
-  download_size: 19700900
-  dataset_size: 58922101
+  download_size: 1777867
+  dataset_size: 3545384
+configs:
+- config_name: full
+  data_files:
+  - split: train
+    path: full/train-*
+  - split: validation
+    path: full/validation-*
+  default: true
+- config_name: light
+  data_files:
+  - split: train
+    path: light/train-*
+  - split: validation
+    path: light/validation-*
 ---
 
 # Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions

ambig_qa.py

@@ -1,150 +0,0 @@
-# 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.
-
-"""AmbigQA: Answering Ambiguous Open-domain Questions"""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{ min2020ambigqa,
-    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
-    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
-    booktitle={ EMNLP },
-    year={2020}
-}
-"""
-
-_DESCRIPTION = """\
-AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with
-14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.
-We provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.
-"""
-_HOMEPAGE = "https://nlp.cs.washington.edu/ambigqa/"
-_LICENSE = "CC BY-SA 3.0"
-
-_URL = "https://nlp.cs.washington.edu/ambigqa/data/"
-_URLS = {
-    "light": _URL + "ambignq_light.zip",
-    "full": _URL + "ambignq.zip",
-}
-
-
-class AmbigQa(datasets.GeneratorBasedBuilder):
-    """AmbigQA dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="light",
-            version=VERSION,
-            description="AmbigNQ light version with only inputs and outputs",
-        ),
-        datasets.BuilderConfig(
-            name="full",
-            version=VERSION,
-            description="AmbigNQ full version with all annotation metadata",
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = "full"
-
-    def _info(self):
-        features_dict = {
-            "id": datasets.Value("string"),
-            "question": datasets.Value("string"),
-            "annotations": datasets.features.Sequence(
-                {
-                    "type": datasets.Value("string"),  # datasets.ClassLabel(names = ["singleAnswer","multipleQAs"])
-                    "answer": datasets.features.Sequence(datasets.Value("string")),
-                    "qaPairs": datasets.features.Sequence(
-                        {
-                            "question": datasets.Value("string"),
-                            "answer": datasets.features.Sequence(datasets.Value("string")),
-                        }
-                    ),
-                }
-            ),
-        }
-        if self.config.name == "full":
-
-            detail_features = {
-                "viewed_doc_titles": datasets.features.Sequence(datasets.Value("string")),
-                "used_queries": datasets.features.Sequence(
-                    {
-                        "query": datasets.Value("string"),
-                        "results": datasets.features.Sequence(
-                            {
-                                "title": datasets.Value("string"),
-                                "snippet": datasets.Value("string"),
-                            }
-                        ),
-                    }
-                ),
-                "nq_answer": datasets.features.Sequence(datasets.Value("string")),
-                "nq_doc_title": datasets.Value("string"),
-            }
-            features_dict.update(detail_features)
-
-        features = datasets.Features(features_dict)
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # download and extract URLs
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        train_file_name = "train.json" if self.config.name == "full" else "train_light.json"
-        dev_file_name = "dev.json" if self.config.name == "full" else "dev_light.json"
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], train_file_name)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], dev_file_name)},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for example in data:
-                id_ = example["id"]
-                annotations = example["annotations"]
-                # Add this because we cannot have None values (all keys in the schema should be present)
-                for an in annotations:
-                    if "qaPairs" not in an:
-                        an["qaPairs"] = []
-                    if "answer" not in an:
-                        an["answer"] = []
-
-                yield id_, example

dataset_infos.json

@@ -1 +0,0 @@
-{"light": {"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n", "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n", "homepage": "https://nlp.cs.washington.edu/ambigqa/", "license": "CC BY-SA 3.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"type": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "qaPairs": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ambig_qa", "config_name": "light", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2739732, "num_examples": 10036, "dataset_name": "ambig_qa"}, "validation": {"name": "validation", "num_bytes": 805808, "num_examples": 2002, "dataset_name": "ambig_qa"}}, "download_checksums": {"https://nlp.cs.washington.edu/ambigqa/data/ambignq_light.zip": {"num_bytes": 1061383, "checksum": "3f5dada69dec05cef1533a64945cd7bafde1aa94b0cdd6fa9a22f881206220db"}, "https://nlp.cs.washington.edu/ambigqa/data/ambignq.zip": {"num_bytes": 18639517, "checksum": "e85cec5909f076c6f584322c7f05cae44dcacaec93758c110a26fcceaa8da0ce"}}, "download_size": 19700900, "post_processing_size": null, "dataset_size": 3545540, "size_in_bytes": 23246440}, "full": {"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n", "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n", "homepage": "https://nlp.cs.washington.edu/ambigqa/", "license": "CC BY-SA 3.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"type": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "qaPairs": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "viewed_doc_titles": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "used_queries": {"feature": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "results": {"feature": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "nq_answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "nq_doc_title": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ambig_qa", "config_name": "full", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43538733, "num_examples": 10036, "dataset_name": "ambig_qa"}, "validation": {"name": "validation", "num_bytes": 15383368, "num_examples": 2002, "dataset_name": "ambig_qa"}}, "download_checksums": {"https://nlp.cs.washington.edu/ambigqa/data/ambignq_light.zip": {"num_bytes": 1061383, "checksum": "3f5dada69dec05cef1533a64945cd7bafde1aa94b0cdd6fa9a22f881206220db"}, "https://nlp.cs.washington.edu/ambigqa/data/ambignq.zip": {"num_bytes": 18639517, "checksum": "e85cec5909f076c6f584322c7f05cae44dcacaec93758c110a26fcceaa8da0ce"}}, "download_size": 19700900, "post_processing_size": null, "dataset_size": 58922101, "size_in_bytes": 78623001}}

full/train-00000-of-00001.parquet

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