add dataset

README.md

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+---
+configs:
+- config_name: tags
+  data_files:
+  - split: train
+    path: "xbrl_tags_train.csv"
+  - split: test
+    path: "xbrl_tags_test.csv"
+- config_name: value
+  data_files:
+  - split: train
+    path: "value_train.csv"
+  - split: test
+    path: "value_test.csv"
+- config_name: formula
+  data_files:
+  - split: train
+    path: "formula_formatted_with_tags_train.csv"
+  - split: test
+    path: "formula_formatted_with_tags_test.csv"
+- config_name: formula_calculations
+  data_files:
+  - split: train
+    path: "formula_calculation_train.csv"
+  - split: test
+    path: "formula_calculation_test.csv"
+---
+
+# XBRL Extraction Dataset

generate_xbrl_extract_hf_split.py

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+import xml.etree.ElementTree as ET
+import re
+import json
+from typing import List, Dict
+from tqdm import tqdm
+import random
+import os.path
+from huggingface_hub import HfApi
+import csv
+
+random.seed(42)
+import subprocess
+
+example_qa_dict = {
+    "xbrl_tags": {
+        "q": "What is the US GAAP XBRL tag for Cash and Cash Equivalents as reported by Example Company Inc for the Fiscal Year ending in FY 2022",
+        "a": "us-gaap:AnExampleTagName"
+    },
+    "value": {
+        "q": "What is the value of Exapmle company's income for the Fiscal year ending in FY 2020?",
+        "a": "80000000"
+    },
+    "formula_calculation": {
+        "q": "Can you provide the formula for Operating Profit Margin from Example Corp for the Fiscal Year ending in FY 2022?",
+        "a": "(50000000 / 3590000000) * 100"
+    },
+    "formula_formatted_with_tags": {
+        "q": "What is the formula for the Gross Profit Margin of Example Inc, formatted with the relevant US GAAP XBRL tags",
+        "a": "us-gaap:ExampleTag / us-gaap:AnotherExampleTag) * 100"
+    }
+}
+
+
+def get_xbrl_dataset(data: List[Dict], cat):
+    """
+    Saves entries with matching category1 or category2 in the format for fine-tuning.
+
+    Args:
+        data (List[Dict]): The input JSON data.
+        category (str): The category name to match.
+    """
+    results = []
+    for entry in data:
+        question = entry["query"]
+        question = re.sub(r"\(.*?\)", "", question)
+        context_ids = entry["contextID"]
+
+        # if not os.path.isfile('train/DowJones30/' + doc_path):
+        #     print(f"missing file {doc_path}")
+        #     continue
+
+        example_qa = f"Example question: {example_qa_dict[cat]['q']}\nExample answer: {example_qa_dict[cat]['a']}"
+        output = entry["raw_answer"]
+
+        if cat == 'formula_calculation':
+            question += " Answer with a formula substituted with values. "
+            output = entry["value_formula_answer"]
+
+        output = str(output)
+        instruction = (f"You are a knowledgeable XBRL assistant. Your task is to analyze the XBRL context and provide an"
+                       f" accurate and very concise answer to the question, The example question can help you to learn the "
+                       f"answer format. DO NOT output xml, code, explanation or create new question. \n{example_qa}\n")
+
+        input = f"Question: {question}\nAnswer:"
+        year = int(entry["{fiscal year/quarter}"].replace("FY ", ""))
+
+        # context_xml = add_xml(instruction + input, doc_path}, context_ids[0])
+        # if len(context_xml) > 24000:
+        #     continue
+
+        # print(entry["answer"])
+        # entry["doc_path"], entry["answer"], entry["contextID"][0]
+        results.append({
+            "instruction": instruction,
+            "input": input,
+            "output": output,
+            "year": year,
+            "company": entry["ticker"],
+            "doc_path": entry['doc_path'],
+            "context_id": context_ids[0],
+        })
+
+    print("final length", len(results))
+    return results
+
+
+def gen_xbrl(cat):
+    with open("xbrl_bench_34020.json", "r") as f:
+        data = json.load(f)
+        filtered_data = [entry for entry in data if entry['category1'] == cat or entry['category2'] == cat]
+
+        all_doc_path = list(set([entry['doc_path'] for entry in filtered_data]))
+        print(f"Total data size for {cat}: {len(filtered_data)}, total number of filings {len(all_doc_path)}")
+        random.shuffle(filtered_data)
+
+        dataset = get_xbrl_dataset(filtered_data, cat)
+
+        test_data = [x for x in dataset if x['year'] == 2023]
+        train_data = [x for x in dataset if x['year'] != 2023]
+
+        print(f"train size: {len(train_data)}, test size: {len(test_data)}\n")
+
+        with open(f"{cat}_test.csv", "w", newline="") as f:
+            w = csv.DictWriter(f, test_data[0].keys(), quoting=csv.QUOTE_ALL)
+            w.writeheader()
+            w.writerows(test_data)
+        with open(f"{cat}_train.csv", "w", newline="") as f:
+            w = csv.DictWriter(f, train_data[0].keys(), quoting=csv.QUOTE_ALL)
+            w.writeheader()
+            w.writerows(train_data)
+
+        return train_data, test_data
+
+
+if __name__ == '__main__':
+    tags_train, tags_test = gen_xbrl("xbrl_tags")
+    value_train, value_test = gen_xbrl("value")
+    formula_train, formula_test = gen_xbrl("formula_formatted_with_tags")
+    formula_calc_train, formula_calc_test = gen_xbrl("formula_calculation")