datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
finance-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.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.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.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/idleengine/big-finance-benchmark.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.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.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.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.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/oliversayshi/big-finance-benchmark.Finance-Questions-Essay_and_Calculation-Chinese
Overview
Finance-Questions-Essay_and_Calculation-Chinese is a carefully curated financial reasoning dataset containing 954 samples, each annotated with high-quality Chain-of-Thought (CoT) reasoning. It is designed to train and evaluate Chinese financial language models on complex essay and calculation tasks.
Stage 1: Data Collection & Standardization
Extract financial question samples from professional textbooks via Easy Dataset.
Manually label 30 seed samples, then use… See the full description on the dataset page: https://huggingface.co/datasets/Anson1110/Finance-Questions-Essay_and_Calculation-Chinese.tiered-finance-eval
Tiered Finance Eval
Twenty agentic finance tasks, each with the reference files an analyst would actually be handed, a
curated gold deliverable, and a tiered, gated rubric that scores a submission against that gold.
Evaluation results for these tasks are published in the companion Space:
CentificAIResearch/Tiered-Finance-Eval.
This dataset holds the tasks only: no model outputs and no scores.
[!IMPORTANT]
Canary string. TIERED-FINANCE-EVAL:d9e2f4a1-7c3b-4e86-9a05-2f1b8c6d40e7… See the full description on the dataset page: https://huggingface.co/datasets/CentificAIResearch/tiered-finance-eval.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.tanpo-finance-sft
Tanpo Finance SFT (10k)
Ownership
Owner: DarkNinja Solutions
Creator: d4rkninja
Community: DarkLab
Domain
Finance and CFO thinking: unit economics, budgeting, forecasting, cash, pricing math, and financial operating cadence.
Dataset summary
Field
Value
Rows
10000
Schema
Chat SFT (messages with system / user / assistant)
Source file
tanpo-finance-sft-10k-format-fixed.jsonl
Audit verdict
PASS
Empty assistant rate… See the full description on the dataset page: https://huggingface.co/datasets/d4rkninja/tanpo-finance-sft.tw-finance-function-call-reasoning
tw-finance-function-call-reasoning
台灣金融場景的繁體中文 function-calling + 推理鏈微調資料集。
欄位規格對齊 twinkle-ai/tw-function-call-reasoning-10k。
⚠️ 使用限制：僅供研究，不得商業使用
本資料集以 CC BY-NC 4.0 授權釋出，僅供學術研究、模型能力探索與方法驗證之用。
請務必理解以下事項後再使用：
不得作商業用途。 包含但不限於：訓練用於對外營利的模型、包裝為付費產品或服務、
作為商業交付物的一部分。若有商業需求，請自行重新建置資料並取得合規來源。
這不是財務、稅務、法律或投資建議。 資料中的稅率、費率、法規門檻雖依 2026 年
（民國 115 年）台灣公開資訊整理，但可能已經過時或有誤。任何實際決策前，
請以主管機關公告為準（財政部、金管會、勞動部、衛福部、中央銀行、全國法規資料庫）。
內容為程式化合成，非真實考題逐字收錄。 題目由模板與參數取樣組合而成，… See the full description on the dataset page: https://huggingface.co/datasets/Simon-Liu/tw-finance-function-call-reasoning.tw-finance-reasoning-instruct
tw-finance-reasoning-instruct
台灣金融知識的繁體中文推理指令資料集。每一題都有完整的思考過程（think）與可查證的答案（output）。
欄位規格對齊 twinkle-ai/tw-reasoning-instruct-50k。
⚠️ 使用限制：僅供研究，不得商業使用
本資料集以 CC BY-NC 4.0 授權釋出，僅供學術研究、模型能力探索與方法驗證之用。
不得作商業用途。 包含訓練用於對外營利的模型、包裝為付費產品或服務、
或作為商業交付物的一部分。若有商業需求，請自行重新建置資料並取得合規來源。
這不是財務、稅務、法律或投資建議。 資料中的稅率、費率、法規門檻依 2026 年
（民國 115 年）台灣公開資訊整理，但可能已經過時。任何實際決策前，
請以主管機關公告為準（財政部、金管會、勞動部、衛福部、中央銀行、全國法規資料庫）。
內容為程式化合成，非真實考題逐字收錄。 題目由計算器與模板生成，
並非任何證照考試的原始試題。… See the full description on the dataset page: https://huggingface.co/datasets/Simon-Liu/tw-finance-reasoning-instruct.ko-finance-asr-corrections
ko-finance-asr-corrections
Frequency-annotated Korean ASR confusion pairs from finance/stock YouTube.
210 pairs
mined from 2,391 videos of auto-captions
across 47 channels
totalling 1,080.1 hours
Each pair carries how often the term was mangled and how often it was said correctly, plus
verification provenance.
한국어 금융·주식 유튜브 자동자막에서 실측한 ASR 오인식→교정 쌍입니다. 모든 쌍에 오표기·정답
표기 빈도(→ 용어별 오인식률)와 검증 메타데이터(2-LLM 합의 감사, 승격 티어)가 붙어 있습니다.
What makes it different
No public… See the full description on the dataset page: https://huggingface.co/datasets/woongstar/ko-finance-asr-corrections.aprm-sft-thoughts-snorkel-finance-policy_best-adamw30-lp0
Act-PRM SFT thoughts — snorkel-finance finance
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT / RL… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-snorkel-finance-policy_best-adamw30-lp0.finance-ops-triage-v0.1
Finance Ops Triage v0.1 dataset
The original small, illustrative dataset prepared for Ugo Chukwu's first Unsloth fine-tuning and deployment exercise. The examples were provided during a guided ChatGPT experiment; they are not collected operational transaction records or an independently validated finance policy.
Code and experiment report · Model archive
Structure
Each JSONL row has messages containing system, user, and assistant entries. The assistant content is… See the full description on the dataset page: https://huggingface.co/datasets/Etherlabs/finance-ops-triage-v0.1.asc-finance-reasoning-20k
Finance Reasoning SFT Dataset — AutoScientist Challenge (20K)
A ~20,018-row chain-of-thought finance-reasoning dataset for supervised fine-tuning — the exact
training set behind the companion model (Mixtral-8x7B-Instruct LoRA, lead entry E24). Built as the
data-recipe submission to the AutoScientist Challenge
(Finance, Part 1, Adaption Labs, 2026) and released under CC-BY-4.0.
It is a 4,018-row curated seed (Adaptive Data quality 9.3/10, grade A) expanded to ~20K with… See the full description on the dataset page: https://huggingface.co/datasets/tejadhith/asc-finance-reasoning-20k.Blum-Finance-Reasoning
BLUM Finance Reasoning
Versioned reasoning examples exported from BLUM Engine at revision
973fa4a3579c8b883372e96ed6e7a6e1c99e534a.
The dataset uses grouped temporal splits. Records from the same thesis lineage never
cross train, validation and test. Secrets, personal identifiers, broker identifiers
and unlicensed verbatim sources are excluded.
Splits
Split
Rows
Start
End
test
53
2026-07-09T05:43:21.697064
2026-07-13T23:47:19.530828
train
416… See the full description on the dataset page: https://huggingface.co/datasets/Italianhype/Blum-Finance-Reasoning.vibethinker-3b-finance-sftmini-data-public-version
VibeThinker-3B Finance-Reader — SFT Training Data · PUBLIC-SAFE subset
🟢 This is vibethinker-3b-finance-sftmini-data-public-version — the redistribution-safe slice of the
full vibethinker-3b-finance-sftmini-data
dataset, containing only US-government public-domain sources (SEC EDGAR family + Federal Register).
Same schema, same pipeline, same teacher — just the legally shareable rows. (Currently private; intended to be made public.)
The supervised fine-tuning dataset… See the full description on the dataset page: https://huggingface.co/datasets/BatuhanECB/vibethinker-3b-finance-sftmini-data-public-version.finance-reasoning-sft-dataset
Personal Finance Reasoning Dataset
A synthetic instruction-tuning dataset designed to teach language models to reason through personal finance and investing decisions using the mental frameworks from classic books in the genre. The goal is not recall of book content but principled reasoning: the model should apply frameworks to novel situations it has never seen.
Source Books
Principles were extracted from the following books:
The Psychology of Money — Morgan Housel
Rich… See the full description on the dataset page: https://huggingface.co/datasets/likhitjuttada/finance-reasoning-sft-dataset.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:… See the full description on the dataset page: https://huggingface.co/datasets/idleengine/Personal-Finance-Queries.personal-finance-chatml-dataset
Bilingual Personal Finance ChatML Dataset (EN/ES)
Dataset Description
This dataset is a professionally curated bilingual (English/Spanish) instruction dataset designed for fine-tuning large language models (LLMs) in the domain of personal finance.
It is structured in ChatML format and intended for supervised fine-tuning (SFT), domain adaptation, and financial instruction modeling.
The dataset is created and reviewed from an accounting perspective, ensuring conceptual… See the full description on the dataset page: https://huggingface.co/datasets/williamjmorenor/personal-finance-chatml-dataset.amalia-Nemotron-SpecializedDomains-Finance-v1
AMALIA Nemotron-SpecializedDomains-Finance-v1
Version of the nvidia/Nemotron-SpecializedDomains-Finance-v1 dataset used in the AMALIA's Supervised Fine-Tuning stage.
This dataset went through a processing pipeline to:
Remove entries that reference other LLMs or research labs;
Remove the reasoning_content field;
Original Dataset: https://huggingface.co/datasets/nvidia/Nemotron-SpecializedDomains-Finance-v1
This dataset is provided as part of the AMALIA project and is… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/amalia-Nemotron-SpecializedDomains-Finance-v1.nihongo-legal-finance-autoscientist-data
nihongo-legal-finance-autoscientist
AutoScientist Challenge entry dataset for Japanese expert QA in the language category.
Intended Use
This dataset is designed for supervised fine-tuning of Japanese assistants that explain
legal and financial concepts with uncertainty, source-awareness, and non-advice caveats.
Columns
instruction: user task
context: background information
response: target answer
rubric: quality expectations
category: subdomain… See the full description on the dataset page: https://huggingface.co/datasets/doraking/nihongo-legal-finance-autoscientist-data.