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rodriguescarson/adaption-financial-qa-with-tables

Financial Filing QA with Reasoning (Augmented) Numerical questions over company-filing tables and text, answered with a worked reasoning path. Rows 11,976 Domain finance Format data.parquet, one row per example Licence other Built for supervised fine-tuning (SFT) experiments on Adaption AutoScientist Columns Column Description prompt The prompt (user turn) as uploaded. completion The target response as uploaded.… See the full description on the dataset page: https://huggingface.co/datasets/rodriguescarson/adaption-financial-qa-with-tables.

sourceHugging Faceotherupdated 5h agoView on Hugging Face
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Dataset Card

Financial Filing QA with Reasoning (Augmented)

Numerical questions over company-filing tables and text, answered with a worked reasoning path.

Rows11,976
Domainfinance
Formatdata.parquet, one row per example
Licenceother
Built forsupervised fine-tuning (SFT) experiments on Adaption AutoScientist

Columns

ColumnDescription
promptThe prompt (user turn) as uploaded.
completionThe target response as uploaded.
enhanced_promptPrompt after Adaption processing (rewrite or augmentation).
enhanced_completionResponse after Adaption processing (rewrite or augmentation).
stratumSampling stratum (e.g. programming language or topic) used to balance the draw.

How it was built

Built from Fino1 reasoning paths over FinQA train, decontaminated against FinQA test pages.

Sources and licence

Notes

  • —This dataset contains the original 1,466-row seed plus rows added by Adaption's augmentation. The added rows are general-purpose and do not all match the dataset's topic; Adaption does not publish their provenance, so the licence is listed as other. The export does not mark which rows are seed and which were added.
  • —Adaption's export for this dataset returns its augmented state, so it is larger than the platform's row count.

Loading

python
from datasets import load_dataset
ds = load_dataset("rodriguescarson/adaption-financial-qa-with-tables", split="train")
python
import pandas as pd
df = pd.read_parquet("hf://datasets/rodriguescarson/adaption-financial-qa-with-tables/data.parquet")

Published by Carson Rodrigues (Hugging Face, Kaggle).