Convert dataset to Parquet

README.md

@@ -68,13 +68,20 @@ dataset_info:
       dtype: int32
   splits:
   - name: train
-    num_bytes: 13496125
+    num_bytes: 13490771
     num_examples: 10006
   - name: test
-    num_bytes: 1731449
+    num_bytes: 1731413
     num_examples: 1342
-  download_size: 5765261
-  dataset_size: 15227574
+  download_size: 7449804
+  dataset_size: 15222184
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for ReDial (Recommendation Dialogues)

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2dd52106a66d484df11c10d06b8ca698917e22e79e0748d55124c2d8b0ea8dd0
+size 834986

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7ff962a8ec261d27be4ea16a909656d5b47f0fd923cdbce93d14208bb8e6b38
+size 6614818

re_dial.py

@@ -1,160 +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.
-"""Annotated dataset of dialogues where users recommend movies to each other."""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{li2018conversational,
-  title={Towards Deep Conversational Recommendations},
-  author={Li, Raymond and Kahou, Samira Ebrahimi and Schulz, Hannes and Michalski, Vincent and Charlin, Laurent and Pal, Chris},
-  booktitle={Advances in Neural Information Processing Systems 31 (NIPS 2018)},
-  year={2018}
-}
-"""
-
-_DESCRIPTION = """\
-ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users
-recommend movies to each other. The dataset was collected by a team of researchers working at
-Polytechnique Montréal, MILA – Quebec AI Institute, Microsoft Research Montréal, HEC Montreal, and Element AI.
-
-The dataset allows research at the intersection of goal-directed dialogue systems
-(such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems.
-"""
-
-_HOMEPAGE = "https://redialdata.github.io/website/"
-
-_LICENSE = "CC BY 4.0 License."
-
-_DATA_URL = "https://github.com/ReDialData/website/raw/data/redial_dataset.zip"
-
-
-class ReDial(datasets.GeneratorBasedBuilder):
-    """Annotated dataset of dialogues where users recommend movies to each other."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        question_features = {
-            "movieId": datasets.Value("string"),
-            "suggested": datasets.Value("int32"),
-            "seen": datasets.Value("int32"),
-            "liked": datasets.Value("int32"),
-        }
-        features = datasets.Features(
-            {
-                "movieMentions": [
-                    {
-                        "movieId": datasets.Value("string"),
-                        "movieName": datasets.Value("string"),
-                    },
-                ],
-                "respondentQuestions": [question_features],
-                "messages": [
-                    {
-                        "timeOffset": datasets.Value("int32"),
-                        "text": datasets.Value("string"),
-                        "senderWorkerId": datasets.Value("int32"),
-                        "messageId": datasets.Value("int32"),
-                    },
-                ],
-                "conversationId": datasets.Value("int32"),
-                "respondentWorkerId": datasets.Value("int32"),
-                "initiatorWorkerId": datasets.Value("int32"),
-                "initiatorQuestions": [question_features],
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_DATA_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train_data.jsonl"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "test_data.jsonl"), "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        with open(filepath, encoding="utf-8") as f:
-            examples = f.readlines()
-            for id_, row in enumerate(examples):
-                data = json.loads(row.strip())
-                d = {}
-                movieMentions_list = []
-                for i in data["movieMentions"]:
-                    d["movieId"] = i
-                    d["movieName"] = data["movieMentions"][i]
-                    movieMentions_list.append(d)
-                    d = {}
-
-                respondentQuestions_list = []
-                for i in data["respondentQuestions"]:
-                    d["movieId"] = i
-                    alpha = data["respondentQuestions"][i]
-                    z = {**d, **alpha}  # merging 2 dictionaries
-                    respondentQuestions_list.append(z)
-                    d = {}
-
-                initiatorQuestions_list = []
-                for i in data["initiatorQuestions"]:
-                    d["movieId"] = i
-                    alpha = data["initiatorQuestions"][i]
-                    z = {**d, **alpha}  # merging 2 dictionaries
-                    initiatorQuestions_list.append(z)
-                    d = {}
-
-                yield id_, {
-                    "movieMentions": movieMentions_list,
-                    "respondentQuestions": respondentQuestions_list,
-                    "messages": data["messages"],
-                    "conversationId": data["conversationId"],
-                    "respondentWorkerId": data["respondentWorkerId"],
-                    "initiatorWorkerId": data["initiatorWorkerId"],
-                    "initiatorQuestions": initiatorQuestions_list,
-                }