datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
financebenchFinanceBench is a first-of-its-kind test suite for evaluating the performance of LLMs on open book financial question answering (QA). This is an open source sample of 150 annotated examples used in the evaluation and analysis of models assessed in the FinanceBench paper.
The PDFs linked in the dataset can be found here as well: https://github.com/patronus-ai/financebench/tree/main/pdfs
The dataset comprises of questions about publicly traded companies, with corresponding answers and evidence… See the full description on the dataset page: https://huggingface.co/datasets/PatronusAI/financebench.finance-tasks
Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024)
This repo contains the evaluation datasets for our paper Adapting Large Language Models via Reading Comprehension.
We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to… See the full description on the dataset page: https://huggingface.co/datasets/AdaptLLM/finance-tasks.Finance-Conversational-Dataset-Indicfinance-alpacaThis dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora
GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: https://github.com/gaurangbharti1/wealth-alpaca… See the full description on the dataset page: https://huggingface.co/datasets/gbharti/finance-alpaca.IndustryCorpus_finance[中文主页]
Industry models play a crucial role in driving enterprise intelligence transformation and innovative development. High-quality industry data is key to improving the performance of large models and realizing industry applications. However, datasets currently used for industry model training generally suffer from issues such as insufficient data volume, low quality, and lack of domain expertise.
To address these problems, we constructed and applied 22 industry data processing operators to… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryCorpus_finance.FinanceBenchThe FinanceBench dataset is derived from the PatronusAI/financebench-test dataset, containing only the PASS examples processed into a clean format for question-answering tasks in the financial domain. FinanceBench-rtl has been repurposed for retrieval.
Usage
import datasets
# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/FinanceBench", "queries")
documents = datasets.load_dataset("embedding-benchmark/FinanceBench", "corpus")
pair_labels =… See the full description on the dataset page: https://huggingface.co/datasets/embedding-benchmark/FinanceBench.Nemotron-SpecializedDomains-Finance-v1
Dataset Description
Nemotron-SpecializedDomains-Finance is a large-scale synthetic financial question-answering dataset designed to improve LLM performance on specialized financial reasoning and document comprehension tasks. The dataset comprises 326K+ high-quality Q&A pairs generated from SEC filings of S&P 500 companies spanning 2019-2024.
This dataset is ready for commercial use.
Overview
The dataset leverages template-based Synthetic Data Generation (SDG) to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SpecializedDomains-Finance-v1.big-finance-benchmark
BigFinanceBench Public Release
arXiv | Website | GitHub | Blog post
Finance answers are only useful when another analyst can audit how they were produced. BigFinanceBench evaluates that full workflow: agents must produce a numerical answer, and their traces are graded against point-weighted rubrics for source choice, period, accounting definition, assumptions, adjustments, and calculation.
This release contains a 50-question stratified subset of the 928-item BigFinanceBench… See the full description on the dataset page: https://huggingface.co/datasets/RogoAI/big-finance-benchmark.FinanceRAG-LinguaFinance-Parallel-Dataset-IndicFinance-Instruct-500k
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/Josephgflowers/Finance-Instruct-500k.news-finance-datasetbig-finance-benchmark
BigFinanceBench Public Release
arXiv | Website | GitHub | Blog post
Finance answers are only useful when another analyst can audit how they were produced. BigFinanceBench evaluates that full workflow: agents must produce a numerical answer, and their traces are graded against point-weighted rubrics for source choice, period, accounting definition, assumptions, adjustments, and calculation.
This release contains a 50-question stratified subset of the 928-item BigFinanceBench… See the full description on the dataset page: https://huggingface.co/datasets/idleengine/big-finance-benchmark.ATLAS-Finance
ATLAS Finance
A benchmark of 100 expert-level tasks inside 13 realistic financial firm environments, packaged in the Harbor RLE format.
Each task drops an AI agent into a Linux workstation with a
persistent multi-app world — inbox, chat, calendar, virtual data room, drive,
wiki — and asks the agent to produce the same deliverable a financial professional would be responsible for:
an Excel workbook containing the model and supporting analysis.
Here we provide the data for this… See the full description on the dataset page: https://huggingface.co/datasets/handshake-ai-research/ATLAS-Finance.IndustryInstruction_Finance-Economics
IndustryInstruction: Finance & Economics
This repository contains the IndustryInstruction: Finance & Economics domain subset of BAAI/IndustryInstruction.
Refer to the parent dataset card for data construction, intended use, limitations,
and licensing details.
Citation
If you use this dataset in your work, please cite IndustryInstruction:
@misc{shi2024industryinstruction,
title = {IndustryInstruction},
author = {Xiaofeng Shi and Lulu Zhao and Hua Zhou… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryInstruction_Finance-Economics.GLM-5.2-Finance-80000x
GLM-5.2 · Finance-80000x
80,000x financial related traces distilled from GLM-5.2 on High reasoning
Risk · Markets · Investments · Corporate Finance · Wealth Management
Token Count: 220M
Unique prompts generated with diffusion Gemma-27B answered by GLM-5.2
You can use this dataset for any purpose and you dont need to credit me, preferably dont claim it as your own.
hi - ianncity
dolma3-6t-sample-10000-docs-finance-and-business
HCAI-Lab/dolma3-6t-sample-10000-docs-finance-and-business
Filename-derived finance_and_business slice of
HCAI-Lab/dolma3-6t-sample-10000-docs, pinned to
revision 561e73c7e0ad35c04f386bae1e3dd39dfb6755e7.
Extraction rule
The corpus contains every source .jsonl.zst file whose filename contains
the literal segment -finance_and_business-. Source paths and compressed file
contents are preserved byte-for-byte. This is a coarse WebOrganizer
finance_and_business category… See the full description on the dataset page: https://huggingface.co/datasets/HCAI-Lab-GT/dolma3-6t-sample-10000-docs-finance-and-business.reddit_finance_43_250k
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:… See the full description on the dataset page: https://huggingface.co/datasets/winddude/reddit_finance_43_250k.timeseries-finance-ETFfinance-deepseek-prompts-distill
We are soon launching an end-to-end data process—distillation and synthetic data—to train (SFT and RL) a financial agentic model!
Financial DeepSeek Distillation Prompts
Ready-to-paste prompts for manually distilling financial reasoning datasets through DeepSeek-V4 pro/flash (or any LLM) UI.
Available Datasets (English)
Dataset
Prompts
Size
Category
Target
finqa_train_prompts.jsonl
6,251
58 MB
Advanced Business Knowledge
2,948… See the full description on the dataset page: https://huggingface.co/datasets/RASSAISAID/finance-deepseek-prompts-distill.big-finance-benchmark
BigFinanceBench Public Release
arXiv | Website | GitHub | Blog post
Finance answers are only useful when another analyst can audit how they were produced. BigFinanceBench evaluates that full workflow: agents must produce a numerical answer, and their traces are graded against point-weighted rubrics for source choice, period, accounting definition, assumptions, adjustments, and calculation.
This release contains a 50-question stratified subset of the 928-item BigFinanceBench… See the full description on the dataset page: https://huggingface.co/datasets/MaitriVasa/big-finance-benchmark.Personal-Finance-Queries
Dataset Description
A curated collection of Reddit posts and top comments focused on personal finance questions. The data is further filtered with the help of LLM-based Voting scores. These scores determine if the query is relevant to a person's financial queries among the other posts of the subreddits.
Dataset Structure
Columns:
category: The sub-domain of personal finance that the query belongs to.
subreddit: Source subreddit (string, categorical)
query: User’s… See the full description on the dataset page: https://huggingface.co/datasets/Akhil-Theerthala/Personal-Finance-Queries.lossbench-finance-v1
LossBench finance-v1
Severity-weighted expected-loss evaluation for agents that touch money. Three finance back-office domains, mechanical ground truth, and a contamination certificate. Models are ranked by what their mistakes cost, not by raw accuracy.
Overview
Task count
2400
Domains
reconciliation, payment_repair, settlement
License
cc-by-4.0
Tasks
Each task is an agentic back-office scenario with a deterministic seed, an… See the full description on the dataset page: https://huggingface.co/datasets/caiotheodoro/lossbench-finance-v1.Turkish-Finance-SFT-Dataset
🇹🇷 Turkish Finance SFT Dataset
Türkçe Finans Alanına Özel Supervised Fine-Tuning (SFT) Dataseti
📋 Dataset Özeti
Bu dataset, Türkçe finans asistanı LLM'lerin eğitimi için özel olarak tasarlanmış, kapsamlı bir Supervised Fine-Tuning (SFT) veri setidir. Kripto para, borsa, teknik analiz, temel analiz, risk yönetimi ve finansal regülasyonlar dahil olmak üzere geniş bir yelpazede yaklaşık 10 milyon token boyutunda soru-cevap çifti verisi içermektedir.
Dataset, hem… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Turkish-Finance-SFT-Dataset.big-finance-benchmark
BigFinanceBench Public Release
arXiv | Website | GitHub | Blog post
Finance answers are only useful when another analyst can audit how they were produced. BigFinanceBench evaluates that full workflow: agents must produce a numerical answer, and their traces are graded against point-weighted rubrics for source choice, period, accounting definition, assumptions, adjustments, and calculation.
This release contains a 50-question stratified subset of the 928-item BigFinanceBench… See the full description on the dataset page: https://huggingface.co/datasets/oliversayshi/big-finance-benchmark.finance-pro-bench
FinancePro-Bench
FinancePro-Bench is a benchmark of 400 complex expert-level finance questions which not only require deep knowledge of finance but also other domains such as regulation, law, strategy, math and code generation. It emulates the kind of nuanced, context specific, multi-step reasoning that expert finance professionals perform such as reviewing accounting judgments, structuring deals, pricing derivatives, navigating tax and compliance edge… See the full description on the dataset page: https://huggingface.co/datasets/Sanscritic/finance-pro-bench.big-finance-benchmark
BigFinanceBench Public Release
arXiv | Website | GitHub | Blog post
Finance answers are only useful when another analyst can audit how they were produced. BigFinanceBench evaluates that full workflow: agents must produce a numerical answer, and their traces are graded against point-weighted rubrics for source choice, period, accounting definition, assumptions, adjustments, and calculation.
This release contains a 50-question stratified subset of the 928-item BigFinanceBench… See the full description on the dataset page: https://huggingface.co/datasets/Koplos/big-finance-benchmark.CFQA_Chinese_Finance_Question_Answering
Citation
For the complete project, please check Here
If you use CFQA in your research, experiments, benchmarks, or publications, please cite the accompanying paper:
@inproceedings{zhu2026cfqa,
title = {CFQA: A Chinese Financial Question Answering Benchmark From Corporate Annual Reports},
author = {Tianning Zhu and Mo Liu and Murathan Kurfali},
booktitle = {Proceedings of The 7th Financial Narrative Processing Workshop (FNP 2026)},
year = {2026},
address =… See the full description on the dataset page: https://huggingface.co/datasets/ZackZhu00/CFQA_Chinese_Finance_Question_Answering.BCCard-Finance-Kor-QnAqa_finance_zhAvailable Versions:
AIR-Bench_24.04
Task / Domain / Language: qa / finance / zh
Available Datasets (Dataset Name: Splits):
default: test
AIR-Bench_24.05
Task / Domain / Language: qa / finance / zh
Available Datasets (Dataset Name: Splits):
default: dev, test