Initial dataset release

.gitattributes

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+data/long_dev.json filter=lfs diff=lfs merge=lfs -text
+data/long_private_test.json filter=lfs diff=lfs merge=lfs -text
+data/long_test.json filter=lfs diff=lfs merge=lfs -text
+data/long_train.json filter=lfs diff=lfs merge=lfs -text

LICENSE.md

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+# Dataset License: CC BY 4.0
+
+The expanded-context dataset in this directory is a modified derivative of
+FinQA and is released under the Creative Commons Attribution 4.0 International
+license (CC BY 4.0).
+
+License reference: https://creativecommons.org/licenses/by/4.0/
+
+When sharing or adapting this dataset:
+
+- give appropriate credit to FinQA and its authors;
+- cite the original FinQA paper;
+- identify this release as modified by expanding the document context;
+- provide a link to CC BY 4.0; and
+- indicate whether further changes were made.
+
+This release must not be described as the original, unmodified FinQA dataset.
+
+The MIT license for original capstone code does not apply to this dataset.

README.md

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+---
+pretty_name: Natively Extended FinQA
+license: cc-by-4.0
+language:
+  - en
+task_categories:
+  - question-answering
+tags:
+  - finance
+  - finqa
+  - financial-question-answering
+  - numerical-reasoning
+  - long-context
+  - rag
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/long_train.json
+      - split: validation
+        path: data/long_dev.json
+      - split: test
+        path: data/long_test.json
+      - split: private_test
+        path: data/long_private_test.json
+---
+
+# Natively Extended FinQA
+
+Natively Extended FinQA is a long-context derivative of FinQA for numerical reasoning over financial data.
+
+It preserves the FinQA task while increasing the amount of financial context surrounding each question.
+
+Average context increased from approximately:
+
+`611 words per question`
+
+to:
+
+`5,629 words per question`
+
+## Splits
+
+| Hugging Face Split | File | Records |
+|---|---|---:|
+| `train` | `long_train.json` | 6,251 |
+| `validation` | `long_dev.json` | 883 |
+| `test` | `long_test.json` | 1,147 |
+| `private_test` | `long_private_test.json` | 919 |
+
+## Load with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-Dataset"
+)
+
+print(dataset)
+print(dataset["train"][0]["qa"]["question"])
+````
+
+To load only one split:
+
+```python
+test = load_dataset(
+    "Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-Dataset",
+    split="test",
+)
+```
+
+## Load as Standard JSON
+
+```python
+import json
+
+with open("data/long_test.json", "r", encoding="utf-8") as f:
+    test = json.load(f)
+```
+
+## Dataset Structure
+
+Important top-level fields include:
+
+```text
+id
+pre_text
+table
+post_text
+qa
+```
+
+Important `qa` fields include:
+
+```text
+question
+program
+exe_ans
+gold_inds
+```
+
+Additional FinQA fields may also be present.
+
+## Field Usage
+
+### Model-Visible Full-Context Input
+
+```text
+qa.question
+pre_text
+table
+post_text
+```
+
+### Supervised Training Target
+
+```text
+qa.program
+```
+
+### Evaluation-Only / Gold Information
+
+Do not expose these fields to a model during normal validation or test inference:
+
+```text
+qa.program
+qa.answer
+qa.exe_ans
+qa.gold_inds
+```
+
+Example:
+
+```python
+def extract_model_input(example):
+    return {
+        "question": example["qa"]["question"],
+        "pre_text": example.get("pre_text", []),
+        "table": example.get("table", []),
+        "post_text": example.get("post_text", []),
+    }
+```
+
+## FinQA Program Format
+
+The associated experiments generate structured FinQA programs.
+
+Example:
+
+```json
+["subtract(", "5829", "5735", ")", "EOF"]
+```
+
+Multi-step programs can reference previous operations:
+
+```json
+[
+  "subtract(",
+  "5829",
+  "5735",
+  ")",
+  "divide(",
+  "#0",
+  "5735",
+  ")",
+  "EOF"
+]
+```
+
+These programs should be evaluated with the FinQA evaluator.
+
+## Dataset Construction
+
+The expanded version adds additional native financial context around FinQA examples.
+
+The goal is to increase context noise while retaining the original question, financial table data, program targets, execution answers, and supporting-evidence information.
+
+Context blocks were varied so relevant evidence would not always occupy a predictable position.
+
+Ordering within individual source blocks was preserved.
+
+## Recommended Usage
+
+```text
+train        -> adapter training
+validation   -> prompt/checkpoint/config selection
+test         -> final evaluation
+private_test -> private-test-style use
+```
+
+If reproducing the accompanying study, do not use the test split for tuning.
+
+## Associated Research
+
+The dataset supports a six-method comparison using:
+
+`Qwen/Qwen2.5-7B-Instruct`
+
+Methods:
+
+* Baseline
+* RAG
+* LoRA
+* QLoRA
+* RAG + LoRA
+* RAG + QLoRA
+
+Research repository:
+
+`MarkPaulRosenthal/Accuracy-Is-Not-Enough-Practical-Financial-QA`
+
+GitHub dataset repository:
+
+[MarkPaulRosenthal/Accuracy-Is-Not-Enough-FinQA-Dataset](https://github.com/MarkPaulRosenthal/Accuracy-Is-Not-Enough-FinQA-Dataset)
+
+## Retrieval Data
+
+The fixed practical retrieval artifacts used by the RAG-family methods are published in the companion research repository rather than this dataset repository.
+
+They are derived model inputs, not primary benchmark splits.
+
+## Intended Uses
+
+Appropriate uses include:
+
+* long-context financial QA
+* numerical reasoning
+* structured program generation
+* retrieval evaluation
+* RAG
+* LoRA/QLoRA
+* context-noise analysis
+* full-context versus retrieved-context comparisons
+
+## Limitations
+
+This is a controlled FinQA-derived benchmark rather than a random sample of complete production financial documents.
+
+Results should not automatically be generalized to arbitrary financial reports or other financial domains without external evaluation.
+
+## License
+
+**CC BY 4.0**
+
+This dataset remains a derivative of FinQA and should be attributed accordingly.
+
+## Attribution
+
+Natively Extended FinQA builds on:
+
+**FinQA: A Dataset of Numerical Reasoning over Financial Data**
+
+Users should cite the original FinQA work when using this derivative dataset.

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