Dataset in parquet format, and README.MD

FinRAG.parquet

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+oid sha256:0e6106354d99c4ed109b886281ad5a838d011f184340fcd156cca1f26e3dd939
+size 20332214

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- question-answering
+- multiple-choice
+language:
+- en
+tags:
+- finance
+- numerical-reasoning
+- table-qa
+- financial-analysis
+size_categories:
+- 10K<n<100K
+---
+
+# Financial Reasoning Dataset with Distractors
+
+## Dataset Description
+
+This dataset contains 12,500 financial reasoning questions based on real-world financial documents, earnings reports, and financial tables. Each question is accompanied by a correct answer and four carefully crafted distractor answers, making it suitable for multiple-choice question answering tasks and assessing financial numerical reasoning capabilities.
+
+### Dataset Summary
+
+- **Total Examples**: 12,500
+- **Format**: Multiple-choice questions with 5 options (1 correct + 4 distractors)
+- **Domain**: Financial documents, earnings reports, financial tables
+- **Task**: Numerical reasoning over financial text and tables
+- **Language**: English
+
+### Source Data
+
+This dataset combines all splits (train, validation, and test) from:
+- **FinQA**: Financial Question Answering dataset (7,750 questions, 62%)
+- **TAT-QA**: Table-and-Text Question Answering dataset (4,750 questions, 38%)
+
+### Distractor Generation
+
+Four distractor answers were algorithmically generated for each question using the following techniques:
+- **Stop early**: Stopping calculation before completion
+- **Negate operand**: Negating numbers in the calculation
+- **Operand bleeding**: Using the wrong operands from the table
+- **Replace operator**: Using the wrong mathematical operation (e.g., multiply instead of add)
+- **Switch order**: Changing the order of operations
+- **Percentage error**: Mistakes in percentage conversion
+- **Unit error**: Mistakes in unit conversion (e.g., millions vs. thousands)
+- **Append operation**: Adding extra unnecessary operations
+- **Substitution error**: Substituting incorrect values from the table
+
+These techniques create plausible but incorrect answers that test true understanding of the financial reasoning task.
+## Dataset Structure
+
+### Data Fields
+
+Each example in the dataset contains:
+
+- **`id`** (string): Unique identifier for each question
+- **`pre_text`** (list of strings): Contextual text passages from the financial document that appear before the table
+- **`post_text`** (list of strings): Additional contextual text passages that appear after the table (may be empty)
+- **`table`** (list of lists): Financial table data in row-major format, where the first row typically contains headers
+- **`question`** (string): The financial reasoning question to be answered
+- **`choices`** (list of strings): List of 5 answer choices (1 correct + 4 distractors), randomly shuffled
+- **`answer`** (integer): Index (0-4) pointing to the correct choice in the `choices` list
+- **`metadata`** (dict): Additional information including:
+  - `instructions`: General instructions for the question type
+  - `date_created`: Date the entry was created
+  - `identifier`: Numeric identifier
+
+### Data Example
+
+```json
+{
+  "id": "9bbb9fb3-3482-4d4d-be40-dd6ff47c23e9",
+  "pre_text": [
+    "Orders at Mobility grew to a record high on a sharp increase in volume...",
+    "Revenue grew slightly as double-digit growth in the customer services business..."
+  ],
+  "post_text": [],
+  "table": [
+    ["", "", "Fiscal year", "", "% Change"],
+    ["(in millions of €)", "2019", "2018", "Actual", "Comp."],
+    ["Orders", "12,894", "11,025", "17 %", "16 %"],
+    ["Revenue", "8,916", "8,821", "1 %", "0 %"]
+  ],
+  "question": "Analyse this data from a financial earnings document. What it the increase / (decrease) in revenue from 2018 to 2019?",
+  "choices": ["-3978", "17737", "94", "95", "1"],
+  "answer": 3,
+  "metadata": {
+    "instructions": "Analyse this data from a financial earnings document.",
+    "date_created": "2024-07-16",
+    "identifier": 1000
+  }
+}
+```
+
+## Citation
+
+**FinQA:**
+```bibtex
+@inproceedings{chen-etal-2021-finqa,
+    title = "{F}in{QA}: A Dataset of Numerical Reasoning over Financial Data",
+    author = "Chen, Zhiyu  and Chen, Wenhu  and Smiley, Charese  and Shah, Sameena  and
+              Borova, Iana  and Langdon, Dylan  and Moussa, Reema  and Beane, Matt  and
+              Huang, Ting-Hao  and Routledge, Bryan  and Wang, William Yang",
+    booktitle = "Proceedings of EMNLP 2021",
+    year = "2021"
+}
+```
+
+**TAT-QA:**
+```bibtex
+@inproceedings{zhu-etal-2021-tat,
+    title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance",
+    author = "Zhu, Fengbin  and Lei, Wenqiang  and Wang, Chao  and Zheng, Jianming  and
+              Poria, Soujanya  and Chua, Tat-Seng",
+    booktitle = "Proceedings of ACL-IJCNLP 2021",
+    year = "2021"
+}
+```