Dataset: LLM Finetune Dataset For Crypto And Blockchain

About this Dataset

LLM Finetune Dataset For Crypto And Blockchain

Overview

This dataset comprises 804 question and answer (Q&A) pairs meticulously curated to cover a broad spectrum of topics within the cryptocurrency and blockchain domain. It's designed to aid in the fine-tuning of Large Language Models (LLMs), enhancing their capabilities in understanding and generating responses related to digital currencies, blockchain technology, smart contracts, decentralized finance (DeFi), and more.

Features

-Number of Entries: 804 Q&A pairs.

-Content: Questions and their corresponding answers related to cryptocurrency and blockchain technology.

-Format: The dataset is structured to facilitate easy import and processing, ideal for machine learning models, especially for fine-tuning of llms via autotrain from huggingface.

-Topics Covered: Includes QnA framed from approximately 66 different research papers covering 3 major topics, Cryptocurrencies, Blockchain and Stock Markets.

Potential Use Cases

-LLM Fine-Tuning: Specifically tailored for researchers and developers looking to enhance LLMs' understanding of and engagement with cryptocurrency and blockchain-related queries.

-Educational Tools: Can serve as a foundation for creating educational content, FAQs, or interactive learning platforms focusing on digital currencies and blockchain.

-Chatbots and Virtual Assistants: Development of specialized virtual assistants for cryptocurrency enthusiasts and investors, providing accurate and contextually relevant information.

Tables

Knowledgebase Pdf Files Crypto Currencies

@kaggle.amalrajsingh_cryptocurrency_blockchain_and_stock_market_qa.knowledgebase_pdf_files_crypto_currencies

6.13 KB
32 rows
3 columns


CREATE TABLE knowledgebase_pdf_files_crypto_currencies (
  "topic" VARCHAR,
  "link" VARCHAR,
  "total_73_papers_across_the_3" VARCHAR
);

Train

@kaggle.amalrajsingh_cryptocurrency_blockchain_and_stock_market_qa.train

146.96 KB
804 rows
4 columns


CREATE TABLE train (
  "unnamed_0" BIGINT,
  "question" VARCHAR,
  "answer" VARCHAR,
  "text" VARCHAR
);