### Introduction:
Welcome to the repository for Prompt Tuning and Prompt Tuning by Chain-of-Thoughts (CoT) models implemented on the QP, QQA, and QNLI datasets. This README provides an overview of the project, its structure, and instructions for usage.

### About Prompt Tuning and Chain-of-Thoughts (CoT):
Prompt Tuning is a method for tuning pretrained language models for specific tasks by using a small number of prompts, or templates, instead of fine-tuning on large task-specific datasets. Chain-of-Thoughts, also known as CoT, extends this idea by allowing prompts to be composed of multiple sequential steps, providing more expressiveness and control over the model's behavior.

### Dataset:
The models are implemented and evaluated on three datasets:
1. **QP (Quantitative Prediction)**: The Quantitative Prediction (QP) dataset refers to a collection of structured data that is utilized for the purpose of quantitative analysis and predictive modeling. This dataset typically contains numerical values and measurements across various variables or features, which are often derived from scientific experiments, observations, or simulations.
2. **QQA (Quantitative Question Answering)**: The Quantitative Question Answering (QQA) dataset is a collection of questions and corresponding answers designed specifically to evaluate machine comprehension and reasoning abilities in understanding quantitative information. These datasets typically contain questions that require understanding and manipulation of numerical data, statistics, mathematical concepts, and logical reasoning to derive the correct answers. QQA datasets often cover various domains such as mathematics, science, finance, and economics, providing a diverse range of quantitative challenges for natural language processing systems.
3. **QNLI (Quantitative Natural Language Inference)**: The Quantitative Natural Language Inference (QNLI) dataset is a benchmark dataset designed for evaluating the performance of natural language understanding models, particularly in the domain of textual entailment. It is derived from the Stanford Question Answering Dataset (SQuAD) and is structured to assess a model's ability to determine if a given hypothesis can be logically inferred from a corresponding premise. QNLI provides pairs of premise-hypothesis statements extracted from the SQuAD dataset, where the hypothesis either entails, contradicts, or is neutral with respect to the premise.


