Duplicate from kaist-ai/CoT-Collection

Co-authored-by: Se June Joo <[email protected]>

.gitattributes

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CoT-Collection.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.
+
+
+import json
+import os
+
+import datasets
+
+
+
+_CITATION = """\
+@article{kim2023cot,
+  title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
+  author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
+  journal={arXiv preprint arXiv:2305.14045},
+  year={2023}
+}
+"""
+
+_DESCRIPTION = """"""
+
+_LICENSE = "CC BY 4.0"
+
+_HOMEPAGE = "https://github.com/kaistAI/CoT-Collection"
+
+_LANGUAGES = {
+    "en": "English",
+}
+# _ALL_LANGUAGES = "all_languages"
+
+
+
+class CoTCollectionMultiConfig(datasets.BuilderConfig):
+    """BuilderConfig for CoTCollectionMultiConfig."""
+
+    def __init__(self, languages=None, **kwargs):
+        super(CoTCollectionMultiConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs),
+        self.languages = languages
+
+
+class CoTCollection(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        CoTCollectionMultiConfig(
+            name=lang,
+            languages=[lang],
+            description=f"{_LANGUAGES[lang]} CoT-Collection data used in the paper 'The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning'",
+        )
+        for lang in _LANGUAGES
+    ]
+    BUILDER_CONFIG_CLASS = CoTCollectionMultiConfig
+    DEFAULT_CONFIG_NAME = "en"
+
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "source": datasets.Value("string"),
+                "target": datasets.Value("string"),
+                "rationale": datasets.Value("string"),
+                "task": datasets.Value("string"),
+                "type": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+
+    def _split_generators(self, dl_manager):
+        train_PATHS = [f"./data/CoT_collection_{lang}.json" for lang in self.config.languages]
+
+        train_paths = dl_manager.download_and_extract(train_PATHS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_paths})
+        ]
+
+    def _generate_examples(self, filepath):
+        for _file in filepath:
+            with open(_file, "r", encoding="utf-8") as fi:
+                data = json.load(fi)
+                buffer = []
+                for idx, value in data.items():
+                    if 'rationale' in value.keys():
+                        buffer.append({
+                            'source': value['source'],
+                            'target': value['target'],
+                            'rationale': value['rationale'],
+                            'task': value['task'],
+                            'type': 'CoT'
+                        })
+                    else:
+                        value['rationale'] = ''
+                        buffer.append({
+                            'source': value['source'],
+                            'target': value['target'],
+                            'rationale': value['rationale'],
+                            'task': value['task'],
+                            'type': 'Direct',
+                        })   
+                
+
+            for idx,dat in enumerate(buffer):
+                yield idx, dat
+    

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+- text-classification
+language:
+- en
+size_categories:
+- 1M<n<10M
+---
+
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:https://github.com/kaistAI/CoT-Collection** 
+- **Repository:https://github.com/kaistAI/CoT-Collection** 
+- **Paper:https://arxiv.org/abs/2305.14045** 
+- **Point of Contact:[email protected]** 
+
+### Dataset Summary
+
+![plot](./cot_collection.JPG)
+
+The CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into language models.
+While proprietary LLMs excel at generating Chain-of-Thoughts based on prompting, smaller LMs do not have this capability. Thus, by fine-tuning to generate Chain-of-Thoughts, it could acquire such abilities.
+
+The CoT Collection provides 1.84 million Chain-of-Thoughts augmented across 1060 tasks from the Flan Collection.\\
+Experimental results show that fine-tuning on the CoT Collection results in (1) better zero-shot performance and (2) a better base model for few-shot learning.
+
+We also provide a multilingual version of CoT Collection at this [link](https://huggingface.co/datasets/kaist-ai/Multilingual-CoT-Collection).
+
+### Supported Tasks and Leaderboards
+
+1060 tasks chosen from the Flan Collection.
+
+
+The list of categories within the CoT Collection are:
+* Natural Language Inference
+* Extractive Question Answering
+* Closed Book Question Answering
+* Science
+* Toxic Classification
+* Arithmetic
+* Program Execution
+* Dialogue
+* Ethics
+* Commonsense Reasoning
+* Multiple Choice Question Answering
+
+### Languages
+
+English
+
+## Dataset Structure
+* source: The input that is given to the language model (LM).
+* target: The ground truth answer to the source.
+* rationale: The Chain of Thought (CoT) that explains how the target could be derived from the source.
+* task: A category that shows which dataset the source and target was extracted from.
+
+In our paper, we trained the underlying language model to generate in the following format:
+```
+\{rationale\}
+[RESULT]
+\{target\}
+```
+Then during evaluation, we parsed the prediction after the phrase ```[RESULT]```.
+
+### Data Splits
+
+|       name        | train |
+|-------------------|------:|
+|CoT-Collection|1837928|
+
+
+
+### Citation Information
+If you find the following model helpful, please considering citing our paper!
+
+```
+@article{kim2023cot,
+  title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
+  author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
+  journal={arXiv preprint arXiv:2305.14045},
+  year={2023}
+}
+```

data/CoT_collection_en.json

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