add dataset card

README.md

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+---
+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
+
+```python
+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.