Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc-by-nc-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- sequence-modeling-other-conversational-curiosity
+---
+
+# Dataset Card for Curiosity Dataset
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Homepage:** [Pedro.ai](https://www.pedro.ai/curiosity)
+- **Repository:**
+- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.emnlp-main.655/)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

curiosity_dialogs.py

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+# 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.
+"""Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{rodriguez2020curiosity,
+    title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},
+    author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},
+    year = 2020,
+    booktitle = {Empirical Methods in Natural Language Processing}
+}
+"""
+
+
+_DESCRIPTION = """\
+This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like
+geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog
+acts, grounding to Wikipedia, and user reactions to messages.
+"""
+
+_HOMEPAGE = "https://www.pedro.ai/curiosity"
+
+_LICENSE = "https://github.com/facebookresearch/curiosity/blob/master/LICENSE"
+
+_URL = "https://obj.umiacs.umd.edu/curiosity/"
+_URLs = {
+    "train": _URL + "curiosity_dialogs.train.json",
+    "val": _URL + "curiosity_dialogs.val.json",
+    "test": _URL + "curiosity_dialogs.test.json",
+    "test_zero": _URL + "curiosity_dialogs.test_zero.json",
+}
+
+
+class CuriosityDialogsConfig(datasets.BuilderConfig):
+    """BuilderConfig for Curiosity Dialogs dataset"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Curiosity Dialogs dataset.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CuriosityDialogsConfig, self).__init__(**kwargs)
+
+
+class CuriosityDialogs(datasets.GeneratorBasedBuilder):
+    """Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        CuriosityDialogsConfig(
+            name="curiosity_dialogs",
+            version=datasets.Version("1.1.0"),
+            description="Curiosity Dialog: A Dataset for Conversational Curiosity",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "messages": datasets.Sequence(
+                        {
+                            "message": datasets.Value("string"),
+                            "liked": datasets.ClassLabel(names=["False", "True"]),
+                            "sender": datasets.ClassLabel(names=["user", "assistant"]),
+                            "facts": datasets.Sequence(
+                                {
+                                    "fid": datasets.Value("int32"),
+                                    "used": datasets.ClassLabel(names=["False", "True"]),
+                                    "source": datasets.ClassLabel(names=["section", "known", "random"]),
+                                }
+                            ),
+                            "message_id": datasets.Value("string"),
+                            "dialog_acts": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                    "known_entities": datasets.Sequence(datasets.Value("string")),
+                    "focus_entity": datasets.Value("string"),
+                    "dialog_id": datasets.Value("int32"),
+                    "inferred_steps": datasets.ClassLabel(names=["False", "True"]),
+                    "created_time": datasets.Value("int64"),
+                    "aspects": datasets.Sequence(datasets.Value("string")),
+                    "first_aspect": datasets.Value("string"),
+                    "second_aspect": datasets.Value("string"),
+                    "shuffle_facts": datasets.ClassLabel(names=["False", "True"]),
+                    "related_entities": datasets.Sequence(datasets.Value("string")),
+                    "tag": datasets.Value("string"),
+                    "user_id": datasets.Value("int32"),
+                    "assistant_id": datasets.Value("int32"),
+                    "is_annotated": datasets.ClassLabel(names=["False", "True"]),
+                    "user_dialog_rating": datasets.Value("int32"),
+                    "user_other_agent_rating": datasets.Value("int32"),
+                    "assistant_dialog_rating": datasets.Value("int32"),
+                    "assistant_other_agent_rating": datasets.Value("int32"),
+                    "reported": datasets.ClassLabel(names=["False", "True"]),
+                    "annotated": datasets.ClassLabel(names=["False", "True"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir["train"]),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="val",
+                gen_kwargs={"filepath": os.path.join(data_dir["val"]), "split": "val"},
+            ),
+            datasets.SplitGenerator(
+                name="test",
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir["test"]),
+                    "split": "test_zero",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test_zero",
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir["test_zero"]),
+                    "split": "test_zero",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        # Bool entries are converted to string entries because of PyArrow error
+        with open(filepath, encoding="utf-8") as f:
+            dataset = json.load(f)
+            dialogs = dataset["dialogs"]
+
+            for id_, data in enumerate(dialogs):
+                messages = data["messages"]
+
+                for message in messages:
+                    message["liked"] = str(message["liked"])
+                    facts = message["facts"]
+
+                    for fact in facts:
+                        fact["used"] = str(fact["used"])
+
+                known_entities = data["known_entities"]
+                focus_entity = data["focus_entity"]
+                dialog_id = data["dialog_id"]
+                inferred_steps = str(data["inferred_steps"])
+                created_time = data["created_time"]
+                aspects = data["aspects"]
+                first_aspect = data["first_aspect"]
+                second_aspect = data["second_aspect"]
+                shuffle_facts = str(data["shuffle_facts"])
+                related_entities = data["related_entities"]
+                tag = data["tag"]
+                user_id = data["user_id"]
+                assistant_id = data["assistant_id"]
+                is_annotated = str(data["is_annotated"])
+                user_dialog_rating = data["user_dialog_rating"]
+                user_other_agent_rating = data["user_other_agent_rating"]
+                assistant_dialog_rating = data["assistant_dialog_rating"]
+                assistant_other_agent_rating = data["assistant_other_agent_rating"]
+                reported = str(data["reported"])
+                annotated = str(data["annotated"])
+
+                yield id_, {
+                    "messages": messages,
+                    "known_entities": known_entities,
+                    "focus_entity": focus_entity,
+                    "dialog_id": dialog_id,
+                    "inferred_steps": inferred_steps,
+                    "created_time": created_time,
+                    "aspects": aspects,
+                    "first_aspect": first_aspect,
+                    "second_aspect": second_aspect,
+                    "shuffle_facts": shuffle_facts,
+                    "related_entities": related_entities,
+                    "tag": tag,
+                    "user_id": user_id,
+                    "assistant_id": assistant_id,
+                    "is_annotated": is_annotated,
+                    "user_dialog_rating": user_dialog_rating,
+                    "user_other_agent_rating": user_other_agent_rating,
+                    "assistant_dialog_rating": assistant_dialog_rating,
+                    "assistant_other_agent_rating": assistant_other_agent_rating,
+                    "reported": reported,
+                    "annotated": annotated,
+                }

dataset_infos.json

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+{"curiosity_dialogs": {"description": "This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like\ngeopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog\nacts, grounding to Wikipedia, and user reactions to messages.\n", "citation": "@inproceedings{rodriguez2020curiosity,\n    title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},\n    author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},\n    year = 2020,\n    booktitle = {Empirical Methods in Natural Language Processing}\n}\n", "homepage": "https://www.pedro.ai/curiosity", "license": "https://github.com/facebookresearch/curiosity/blob/master/LICENSE", "features": {"messages": {"feature": {"message": {"dtype": "string", "id": null, "_type": "Value"}, "liked": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sender": {"num_classes": 2, "names": ["user", "assistant"], "names_file": null, "id": null, "_type": "ClassLabel"}, "facts": {"feature": {"fid": {"dtype": "int32", "id": null, "_type": "Value"}, "used": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}, "source": {"num_classes": 3, "names": ["section", "known", "random"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "length": -1, "id": null, "_type": "Sequence"}, "message_id": {"dtype": "string", "id": null, "_type": "Value"}, "dialog_acts": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "known_entities": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "focus_entity": {"dtype": "string", "id": null, "_type": "Value"}, "dialog_id": {"dtype": "int32", "id": null, "_type": "Value"}, "inferred_steps": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}, "created_time": {"dtype": "int64", "id": null, "_type": "Value"}, "aspects": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "first_aspect": {"dtype": "string", "id": null, "_type": "Value"}, "second_aspect": {"dtype": "string", "id": null, "_type": "Value"}, "shuffle_facts": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}, "related_entities": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "tag": {"dtype": "string", "id": null, "_type": "Value"}, "user_id": {"dtype": "int32", "id": null, "_type": "Value"}, "assistant_id": {"dtype": "int32", "id": null, "_type": "Value"}, "is_annotated": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}, "user_dialog_rating": {"dtype": "int32", "id": null, "_type": "Value"}, "user_other_agent_rating": {"dtype": "int32", "id": null, "_type": "Value"}, "assistant_dialog_rating": {"dtype": "int32", "id": null, "_type": "Value"}, "assistant_other_agent_rating": {"dtype": "int32", "id": null, "_type": "Value"}, "reported": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}, "annotated": {"num_classes": 2, "names": ["False", "True"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "curiosity_dialogs", "config_name": "curiosity_dialogs", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 37198297, "num_examples": 10287, "dataset_name": "curiosity_dialogs"}, "val": {"name": "val", "num_bytes": 4914487, "num_examples": 1287, "dataset_name": "curiosity_dialogs"}, "test": {"name": "test", "num_bytes": 4915613, "num_examples": 1287, "dataset_name": "curiosity_dialogs"}, "test_zero": {"name": "test_zero", "num_bytes": 4333191, "num_examples": 1187, "dataset_name": "curiosity_dialogs"}}, "download_checksums": {"https://obj.umiacs.umd.edu/curiosity/curiosity_dialogs.train.json": {"num_bytes": 66851161, "checksum": "afe5bb0db8406663a316cd02cd25f33214725aaf1e25137cac3a13c6b1b4e70b"}, "https://obj.umiacs.umd.edu/curiosity/curiosity_dialogs.val.json": {"num_bytes": 8772171, "checksum": "4371152e4c783db3fd7366cee6aac0537fa786180a19d48f1ec052181678214f"}, "https://obj.umiacs.umd.edu/curiosity/curiosity_dialogs.test.json": {"num_bytes": 8771731, "checksum": "61dda16362474488c110a4a7d40da1fd7c6fd3c62dddbedea5e3e1fa590e416d"}, "https://obj.umiacs.umd.edu/curiosity/curiosity_dialogs.test_zero.json": {"num_bytes": 7774102, "checksum": "71f9645727393371ba278311fd2bba70a7e469dbafe5afbea90a2ffd7658ee0c"}}, "download_size": 92169165, "post_processing_size": null, "dataset_size": 51361588, "size_in_bytes": 143530753}}

dummy/curiosity_dialogs/1.1.0/dummy_data.zip

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