Datasets:

sharick008
/

convfinqa

language:
  - en
license: mit
task_categories:
  - question-answering
  - table-question-answering
tags:
  - finance
  - conversational-qa
  - numerical-reasoning
  - financial-tables
  - 10-k
pretty_name: ConvFinQA (Tomoro pre-cleaned)
size_categories:
  - 1K<n<10K

ConvFinQA (Tomoro pre-cleaned)

Re-host of the pre-cleaned ConvFinQA dataset distributed as part of Tomoro AI's "Applied AI Solution Engineer" take-home exercise. ConvFinQA itself is the conversational QA benchmark over single-page financial documents from Chen et al. (EMNLP 2022).

The data is a single JSON file with two splits, train (3,037 records) and dev (421 records). Each record carries:

id: stable per-record identifier
doc: a 10-K page split into pre_text, post_text, and a column-keyed table (nested dict: column header → row header → cell value)
dialogue: conv_questions, conv_answers, turn_program (FinQA DSL), executed_answers (the dataset's gold executed values, numeric or 'yes'/'no'), and qa_split (origin flag for hybrid two-source dialogues)
features: precomputed flags (num_dialogue_turns, has_type2_question, has_duplicate_columns, has_non_numeric_values)

Loading

import json
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="sharick008/convfinqa",
    filename="convfinqa_dataset.json",
    repo_type="dataset",
)
data = json.load(open(path))
print(len(data["train"]), len(data["dev"]))

Citation

Chen, Z., Li, S., Smiley, C., Ma, Z., Shah, S., & Wang, W. Y. (2022). ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering. EMNLP.

Licence

ConvFinQA upstream is MIT-licensed. This re-host preserves that licence and the original attribution to the ConvFinQA authors. Tomoro's pre-cleaning passes (column disambiguation, scale normalisation, numeric coercion) are applied on top.