Duplicate from Hello-SimpleAI/HC3

Co-authored-by: beyond <beyond@users.noreply.huggingface.co>

.gitattributes

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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HC3.py

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+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Human ChatGPT Comparison Corpus (HC3)
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/Hello-SimpleAI/chatgpt-comparison-detection"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_BASE_URL = 'https://huggingface.co/datasets/Hello-SimpleAI/HC3/resolve/main/'
+# _URLS = {
+#     "all":"https://huggingface.co/datasets/Hello-SimpleAI/chatgpt-comparison-corpus/resolve/main/en/all.jsonl",
+#     "reddit_eli5": "https://huggingface.co/datasets/Hello-SimpleAI/chatgpt-comparison-corpus/resolve/main/en/reddit_eli5.jsonl",
+#     "wikipedia_csai": "https://huggingface.co/datasets/Hello-SimpleAI/chatgpt-comparison-corpus/resolve/main/en/wikipedia_csai.jsonl",
+#     "open_questions": "https://huggingface.co/datasets/Hello-SimpleAI/chatgpt-comparison-corpus/resolve/main/en/open_questions.jsonl",
+#     "finance": "https://huggingface.co/datasets/Hello-SimpleAI/chatgpt-comparison-corpus/resolve/main/en/finance.jsonl",
+#     "medicine": "https://huggingface.co/datasets/Hello-SimpleAI/chatgpt-comparison-corpus/resolve/main/en/medicine.jsonl",
+# }
+
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """Human ChatGPT Comparison Corpus (HC3)"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="all", version=VERSION, description="concatenation of all other subsets"),
+        datasets.BuilderConfig(name="reddit_eli5", version=VERSION, description="reddit"),
+        datasets.BuilderConfig(name="wiki_csai", version=VERSION, description="wiki"),
+        datasets.BuilderConfig(name="open_qa", version=VERSION, description="open_questions"),
+        datasets.BuilderConfig(name="finance", version=VERSION, description="finance"),
+        datasets.BuilderConfig(name="medicine", version=VERSION, description="medicine"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "all":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "id":datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "human_answers": datasets.Sequence(datasets.Value("string")),
+                    "chatgpt_answers": datasets.Sequence(datasets.Value("string")),
+                    "source": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features(
+                {
+                    "id":datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "human_answers": datasets.Sequence(datasets.Value("string")),
+                    "chatgpt_answers": datasets.Sequence(datasets.Value("string")),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        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, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # 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):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        
+        # urls = _URLS[self.config.name]
+        urls = _BASE_URL+self.config.name+'.jsonl'
+        data_path = dl_manager.download_and_extract(urls) # for online datasets
+        # data_path = _URLS[self.config.name]
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    # "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "filepath": data_path,
+                    "split": "train",
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
+            #         "split": "dev",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test"
+            #     },
+            # ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, 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.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "all":
+                    # Yields examples as (key, example) tuples
+                    yield key, {
+                        "id": key,
+                        "question": data["question"],
+                        "human_answers": data["human_answers"],
+                        "chatgpt_answers": data["chatgpt_answers"],
+                        "source": data["source"]
+                    }
+                else:
+                    yield key, {
+                        "id": key,
+                        "question": data["question"],
+                        "human_answers": data["human_answers"],
+                        "chatgpt_answers": data["chatgpt_answers"],
+                    }

README.md

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+---
+task_categories:
+- text-classification
+- question-answering
+- sentence-similarity
+- zero-shot-classification
+language:
+- en
+- zh
+tags:
+- ChatGPT
+- SimpleAI
+- Detection
+- OOD
+size_categories:
+- 10K<n<100K
+license: cc-by-sa-4.0
+---
+
+
+# Human ChatGPT Comparison Corpus (HC3)
+We propose the first human-ChatGPT comparison corpus, named **HC3** dataset.
+
+This dataset is introduced in our paper: 
+- Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597)
+
+Code, models and analysis are available on our GitHub:
+- GitHub: [**Chatgpt-Comparison-Detection project** 🔬](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)
+
+
+# Dataset Copyright
+If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same. If not, they follow CC-BY-SA license.
+See [dataset copyright](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection#dataset-copyright).
+
+
+# Citation
+
+Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597)
+
+```
+@article{guo-etal-2023-hc3,
+    title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
+    author = "Guo, Biyang  and
+      Zhang, Xin  and
+      Wang, Ziyuan  and
+      Jiang, Minqi  and
+      Nie, Jinran  and
+      Ding, Yuxuan  and
+      Yue, Jianwei  and
+      Wu, Yupeng",
+    journal={arXiv preprint arxiv:2301.07597}
+    year = "2023",
+}
+```

all.jsonl

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reddit_eli5.jsonl

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