Content

This dataset is about the sales of financial products. This dataset contains 7 variables and 9939 observations.

Columns 1. Agent ID-(the agent identifier). It is in alpha numeric format 2. Call ID-(the unique identifier of the call). It is in alpha numeric format. 3. Customer ID- (the customer identifier). It is in alpha numeric format 4. Pickup-(1 if the customer picked up the phone, 0 otherwise). It is in numeric format. 5. Duration-(in seconds). It is in numeric format. 6. ProductSold-how many products agents sold to the customer. It is in numeric format. 7. AgentName-the name of the agent.

Paper |Homepage |Github

  🛠️ Usage

Regarding the data, first of all, you should download the MMfin.tsv and MMfin_CN.tsv files, as well as the relevant financial images. The folder structure is shown as follows: ├─ datasets ├─ images ├─ MMfin ... ├─ MMfin_CN ... │ MMfin.tsv │ MMfin_CN.tsv

The following is the process of inference and evaluation (Qwen2-VL-2B-Instruct as an example): export LMUData="The path of the datasets" python… See the full description on the dataset page: https://huggingface.co/datasets/hithink-ai/MME-Finance.

Finance-Instruct-500k Dataset

  Overview

Finance-Instruct-500k is a comprehensive and meticulously curated dataset designed to train advanced language models for financial tasks, reasoning, and multi-turn conversations. Combining data from numerous high-quality financial datasets, this corpus provides over 500,000 entries, offering unparalleled depth and versatility for finance-related instruction tuning and fine-tuning. The dataset includes content tailored for financial… See the full description on the dataset page: https://huggingface.co/datasets/oieieio/Finance-Instruct-500k.

These three datasets provide closing price information for the following assets: Google, Apple, Microsoft, Netflix, Amazon, Pfizer, Astra Zeneca, Johnson & Johnson, ETH, BTC and LTC.The time period spans from 2012 to the end of 2020.

Explore global financial development data including remittance inflows, bank assets, loans, insurance premiums, stock market indicators, and more. Analyze trends in India, Qatar, Saudi Arabia, and other countries with the World Bank dataset.

Remittance inflows to GDP, Foreign bank assets, Global leasing volume, Private debt securities, Bank Z-score, Loans requiring collateral, Stock price volatility, Bank cost to income ratio

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

The Agricultural Finance Databook is a compilation of various data on current developments in agricultural finance. Large portions of the data come from regular surveys conducted by the Board of Governors of the Federal Reserve System or by Federal Reserve Banks. Other portions come from the quarterly Call Report data of commercial banks or from the reports of other financial institutions involved in agricultural lending. This data is no longer published by the Federal Reserve Board. On October 1, 2010, the E.15 statistical release transitioned from the Board of Governors to the Federal Reserve Bank of Kansas City. You can now find the most current Agricultural Finance Databook at https://www.kansascityfed.org/research/indicatorsdata/agfinancedatabook.

Financefinesse1v1/Finance dataset hosted on Hugging Face and contributed by the HF Datasets community

This is a small budget vs actual expenses dataset based on 12-months.

Francis Financial is a reputable financial services company that provides a range of products and services to its clients. The company's data holdings are vast and varied, encompassing financial market data, economic trends, and industry insights. With a strong focus on serving its clients' needs, Francis Financial's data repository is a treasure trove of valuable information for anyone looking to gain a deeper understanding of the financial world.

From company reports and financial statements to market analysis and industry news, Francis Financial's data collection is a comprehensive archive of important financial information. By leveraging this data, users can gain valuable insights into market trends, spot emerging patterns, and make informed decisions. With its extensive data holdings and commitment to providing high-quality information, Francis Financial is an important player in the financial data landscape.

Graph and download economic data for All Employees, Finance and Insurance (CES5552000001) from Jan 1990 to Feb 2025 about finance, insurance, financial, establishment survey, employment, and USA.

Personal Finance Tools Market is Segmented by Type( Web-Based, Mobile-Based Software ), by End-User Industry (Small Businesses Users, Individual Consumers), and Geography.

Dataset Description 📊🔍

The Sujet-Finance-QA-Vision-100k is a comprehensive dataset containing over 100,000 question-answer pairs derived from more than 9,800 financial document images. This dataset is designed to support research and development in the field of financial document analysis and visual question answering.

  Key Features:

🖼️ 9,801 unique financial document images ❓ 107,050 question-answer pairs 🇬🇧 English language 📄 Diverse financial document… See the full description on the dataset page: https://huggingface.co/datasets/sujet-ai/Sujet-Finance-QA-Vision-100k.

Yahoo.com was the most-visited finance-related website worldwide, with an average of 4.9 billion visits per month during the period between April 2022 and January 2024. Paypal.com was ranked second with 786.8 million monthly visits, while Caixa.gov.br was ranked third, with 292.87 million average accesses, being the leading domain among the world's most-visited banking websites.

This data contains the latest State and Local Government Finance data from the U.S. Census. A detailed description of the project can be found in: Pierson K., Hand M., and Thompson F. (2015). The Government Finance Database: A Common Resource for Quantitative Research in Public Financial Analysis. PLoS ONE doi: 10.1371/journal.pone.0130119

Image generated by DALL-E. See prompt for more details

  💼 📊 Synthetic Financial Domain Documents with PII Labels

gretelai/synthetic_pii_finance_multilingual is a dataset of full length synthetic financial documents containing Personally Identifiable Information (PII), generated using Gretel Navigator and released under Apache 2.0. This dataset is designed to assist with the following use cases:

🏷️ Training NER (Named Entity Recognition) models to detect and label PII in… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_pii_finance_multilingual.

Graph and download economic data for Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Level (BOGZ1FL613065105A) from 1945 to 2024 about finance companies, companies, finance, mortgage, financial, assets, housing, and USA.

reddit finance 43 250k

reddit_finance_43_250k is a collection of 250k post/comment pairs from 43 financial, investing and crypto subreddits. Post must have all been text, with a length of 250chars, and a positive score. Each subreddit is narrowed down to the 70th qunatile before being mergered with their top 3 comments and than the other subs. Further score based methods are used to select the top 250k post/comment pairs.

The code to recreate the dataset is here: https://github.com/getorca/ProfitsBot_V0_OLLM/tree/main/ds_builder

Enhancing Financial Market Predictions: Causality-Driven Feature Selection FinSen dataset that revolutionizes financial market analysis by integrating economic and financial news articles from 197 countries with stock market data. The dataset’s extensive coverage spans 15 years from 2007 to 2023 with temporal information, offering a rich, global perspective 160,000 records on financial market news. Our study leverages causally validated sentiment scores and LSTM models to enhance market forecast accuracy and reliability.

Austin Finance Online is the City of Austin's Financial Services Departments financial transparency application. Austin Finance Online (AFO) provides citizens, media, City staff, and other interested parties with one easily accessible financial data source. AFO not only provides what you would expect from a financial portal - Budget Documents, CAFRs, Official Statements and contact links for offices that comprise the Financial Services Department - but also serves as a switchboard to give users direct access to specialized capabilities. AFO contains three special-purpose modules - eCheckbook, Contract Catalog and Vendor Connection.