Convert dataset to Parquet

README.md

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+---
+dataset_info:
+- config_name: mutual
+  features:
+  - name: answers
+    dtype: string
+  - name: options
+    sequence: string
+  - name: article
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 5141602
+    num_examples: 7088
+  - name: test
+    num_bytes: 634396
+    num_examples: 886
+  - name: validation
+    num_bytes: 624271
+    num_examples: 886
+  download_size: 2773429
+  dataset_size: 6400269
+- config_name: mutual_plus
+  features:
+  - name: answers
+    dtype: string
+  - name: options
+    sequence: string
+  - name: article
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4921179
+    num_examples: 7088
+  - name: test
+    num_bytes: 606620
+    num_examples: 886
+  - name: validation
+    num_bytes: 597340
+    num_examples: 886
+  download_size: 2494284
+  dataset_size: 6125139
+configs:
+- config_name: mutual
+  data_files:
+  - split: train
+    path: mutual/train-*
+  - split: test
+    path: mutual/test-*
+  - split: validation
+    path: mutual/validation-*
+- config_name: mutual_plus
+  data_files:
+  - split: train
+    path: mutual_plus/train-*
+  - split: test
+    path: mutual_plus/test-*
+  - split: validation
+    path: mutual_plus/validation-*
+---

mutual/test-00000-of-00001.parquet

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+size 279020

mutual/train-00000-of-00001.parquet

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+size 2219444

mutual/validation-00000-of-00001.parquet

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+size 274965

mutual_harness.py

@@ -1,136 +0,0 @@
-# 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.
-"""MuTual dataset."""
-
-
-import json
-import os
-from pathlib import Path
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{mutual,
-    title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
-    author = "Cui, Leyang  and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
-    booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
-    year = "2020",
-    publisher = "Association for Computational Linguistics",
-}
-"""
-
-_DESCRIPTION = """\
-MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is
-modified from Chinese high school English listening comprehension test data.
-"""
-
-_HOMEPAGE = "https://github.com/Nealcly/MuTual"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-_URLS = "https://github.com/Nealcly/MuTual/archive/master.zip"
-
-
-class Mutual(datasets.GeneratorBasedBuilder):
-    """MuTual: A Dataset for Multi-Turn Dialogue Reasoning"""
-
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="mutual", version=VERSION, description="The MuTual dataset."
-        ),
-        datasets.BuilderConfig(
-            name="mutual_plus",
-            version=VERSION,
-            description="MuTualPlus is a more difficult MuTual that replaces positive responses with a safe responses.",
-        ),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "answers": datasets.Value("string"),
-                "options": datasets.features.Sequence(datasets.Value("string")),
-                "article": datasets.Value("string"),
-                "id": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=f"{_DESCRIPTION}\n{self.config.description}",
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls = _URLS
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "basepath": os.path.join(
-                        data_dir, "MuTual-master", "data", self.config.name, "train"
-                    ),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "basepath": os.path.join(
-                        data_dir, "MuTual-master", "data", self.config.name, "test"
-                    ),
-                    "split": "test",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "basepath": os.path.join(
-                        data_dir, "MuTual-master", "data", self.config.name, "dev"
-                    ),
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, basepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        key = 0
-        for file in sorted(Path(basepath).iterdir()):
-            if file.suffix != ".txt":
-                continue
-            with open(file, "r", encoding="utf-8") as f:
-                data_str = f.read()
-                # Ignore the occasional empty file.
-                if not data_str:
-                    continue
-                data = json.loads(data_str)
-                yield key, {
-                    "answers": data["answers"],
-                    "options": data["options"],
-                    "article": data["article"],
-                    "id": data["id"],
-                }
-                key += 1

mutual_plus/test-00000-of-00001.parquet

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+size 250869

mutual_plus/train-00000-of-00001.parquet

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+size 1996274

mutual_plus/validation-00000-of-00001.parquet

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+size 247141