datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Argimi-Ardian-Finance-10k-text
The ArGiMI Ardian datasets : Text-only version
The ArGiMi project is committed to open-source principles and data sharing.
Thanks to our generous partners, we are releasing several valuable datasets to the public.
Dataset description
This text-only dataset comprises 34,000 financial annual reports, written in English, meticulously
extracted from their original PDF format to provide a valuable resource for researchers and developers in financial
analysis and natural… See the full description on the dataset page: https://huggingface.co/datasets/artefactory/Argimi-Ardian-Finance-10k-text.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.modeling_valuation_knowledge
Finance Training Data Repository
A curated collection of financial modeling courses, materials, and resources designed to serve as training data for building a finance industry knowledge base.
Repository Structure
Finance_Training_Data/
├── 01_Financial_Statement_Modeling/ # 3-statement modeling fundamentals
├── 02_DCF_Modeling/ # Discounted cash flow valuation
├── 03_Trading_Comps/ # Comparable company analysis
├──… See the full description on the dataset page: https://huggingface.co/datasets/financeindustryknowledgeskills/modeling_valuation_knowledge.vidore_v3_finance_enViDoRe V3 : Finance - EN
This dataset, Financial_Bank_Reports, is a corpus of annual reports from the banking sector, intended for long-document understanding tasks. It is one of the 10 corpora comprising the ViDoRe v3 Benchmark.
About ViDoRe v3
ViDoRe V3 is our latest benchmark for RAG evaluation on visually-rich documents from real-world applications. It features 10 datasets with, in total, 26,000 pages and 3099 queries, translated into 6 languages. Each query comes with… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_en.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.synthetic_pii_finance_multilingual
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.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.IndustryCorpus2_finance_economics
IndustryCorpus2: Finance & Economics
This repository contains the IndustryCorpus2: Finance & Economics domain subset of BAAI/IndustryCorpus2.
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 IndustryCorpus2:
@misc{shi2024industrycorpus2,
title = {IndustryCorpus2},
author = {Xiaofeng Shi and Lulu Zhao and Hua Zhou and Donglin Hao},
year… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryCorpus2_finance_economics.vidore_v3_finance_frViDoRe V3 : Finance - FR
This dataset, Finance - FR, is a corpus of reports from french companies in the luxury domain, intended for long-document understanding tasks. It is one of the 10 corpora comprising the ViDoRe v3 Benchmark.
About ViDoRe v3
ViDoRe V3 is our latest benchmark for RAG evaluation on visually-rich documents from real-world applications. It features 10 datasets with, in total, 26,000 pages and 3099 queries, translated into 6 languages. Each query comes with… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_fr.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.vidore_v3_finance_en_mteb_format
Vidore3FinanceEnRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_finance_en
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3FinanceEnRetrieval")
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_en_mteb_format.FineWeb2024
FineWeb-Edu 2024 — Cleaned and Shuffled
This dataset is a year-specific, cleaned, shuffled, and sharded release derived from HuggingFaceFW/fineweb-edu. It contains English educational web text collected in Common Crawl snapshots whose dump identifier belongs to 2024.
This is not a news-only dataset. The year refers to the Common Crawl capture year, not necessarily the page's publication year.
Dataset summary
Item
Value
Year
2024
Rows
162,500,784… See the full description on the dataset page: https://huggingface.co/datasets/Yahoo-Finance-News/FineWeb2024.Sujet-Finance-Instruct-177k
Sujet Finance Dataset Overview
The Sujet Finance dataset is a comprehensive collection designed for the fine-tuning of Language Learning Models (LLMs) for specialized tasks in the financial sector. It amalgamates data from 18 distinct datasets hosted on HuggingFace, resulting in a rich repository of 177,597 entries. These entries span across seven key financial LLM tasks, making Sujet Finance a versatile tool for developing and enhancing financial applications of AI.… See the full description on the dataset page: https://huggingface.co/datasets/sujet-ai/Sujet-Finance-Instruct-177k.finance-inoculation-midtrainingbig-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.vidore_v3_finance_fr_mteb_format
Vidore3FinanceFrRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_finance_fr
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3FinanceFrRetrieval")
evaluator… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_finance_fr_mteb_format.FinanceRAG-LinguaFinQAFinTruthQATATQAFinance-Parallel-Dataset-IndicTradeTheEventNewsfinance_agent_benchmark
Finance Agent Benchmark Dataset
We present the Finance Agent Benchmark, featuring challenging and diverse real-world finance research problems which require LLMs to perform complex analysis with the use of of recent SEC filings.
We construct the benchmark using a taxonomy of nine financial task categories, developed in consultation with experts from banks, hedge funds, and private equity firms. The dataset includes 537 expert-authored questions, covering tasks from information… See the full description on the dataset page: https://huggingface.co/datasets/vals-ai/finance_agent_benchmark.FinanceIQFinance-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-datasetMTBench_finance_QA_long
📊 MTBench: A Multimodal Time Series Benchmark
MTBench (Huggingface, Github, Arxiv) is a suite of multimodal datasets for evaluating large language models (LLMs) in temporal and cross-modal reasoning tasks across finance and weather domains.
Each benchmark instance aligns high-resolution time series (e.g., stock prices, weather data) with textual context (e.g., news articles, QA prompts), enabling research into temporally grounded and multimodal understanding.
Finance… See the full description on the dataset page: https://huggingface.co/datasets/GGLabYale/MTBench_finance_QA_long.