Introducing CoT-Collection: Enhancing Machine Learning Models
The CoT-Collection is a comprehensive repository that supports the research paper titled "The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning." This project is significant in the domain of language models, offering critical insights and advancements for those interested in machine learning and artificial intelligence. Comprising 1.84 million Chain-of-Thought (CoT) rationales extracted across 1,060 tasks, the CoT-Collection plays a pivotal role in enhancing the learning capabilities of language models with zero-shot and few-shot learning techniques.
Accessing the Dataset
For enthusiasts and researchers wishing to explore the CoT-Collection, the dataset is conveniently available through the Hugging Face datasets library. It can be effortlessly downloaded using the following code snippet:
from datasets import load_dataset
dataset = load_dataset("kaist-ai/CoT-Collection")
This accessibility allows users to engage with a vast repository of data, facilitating experimentation and development.
Model Checkpoint Access
The project goes a step further by providing access to CoT-T5 models that have been trained using the CoT-Collection. These models can be accessed via the Hugging Face transformers library, enabling users to leverage advanced pre-trained models for their research or applications. Here’s how you can load the tokenizers and models:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("kaist-ai/CoT-T5-11B")
model = AutoModelForSeq2SeqLM.from_pretrained("kaist-ai/CoT-T5-11B")
It is important to note that CoT-T5 is available in both 11 billion and 3 billion parameter versions, providing flexibility based on the needs and computational resources available.
Ongoing Code Refactoring
The CoT-Collection team is committed to continually improving the repository. They are in the process of refining their code, with plans to update it soon. This indicates a dedication to quality and user-friendliness.
Rationale Augmentation
The CoT-Collection also includes features for rationale augmentation, crucial for data enhancement. By running specific scripts for each subset of the dataset, users can generate rationales. Initially, users will need to provide OpenAI API keys, which will be stored locally for convenience in future sessions.
The augmentation process yields detailed results for each instance, stored in structured directories. Upon completion, the combined data can be accessed, allowing for comprehensive analysis.
Usage and Licensing
CoT Collection is strictly for non-commercial use, bound by OpenAI’s Terms of Use regarding generated data. This ensures that ethical and legal standards are upheld. For any potential violations or concerns, the project encourages proactive contact.
How to Cite
Researchers and practitioners who benefit from the CoT-Collection are encouraged to cite the paper to acknowledge the contribution of the authors and support ongoing research:
@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}
}
Contact Information
For any queries regarding implementation or content, interested parties can reach out to Seungone Kim at KAIST via email at [email protected]. This ensures support is available for anyone needing further clarification or assistance.
In summary, the CoT-Collection is an invaluable resource for those looking to delve deep into the world of language models and machine learning, backed by a dedicated research team and a vibrant community.