Dannong Wang
commited on
Commit
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d642557
1
Parent(s):
da76932
add dataset
Browse files- README.md +29 -0
- formula_calculation_test.csv +0 -0
- formula_calculation_train.csv +0 -0
- formula_formatted_with_tags_test.csv +0 -0
- formula_formatted_with_tags_train.csv +0 -0
- generate_xbrl_extract_hf_split.py +119 -0
- value_test.csv +0 -0
- value_train.csv +0 -0
- xbrl_tags_test.csv +0 -0
- xbrl_tags_train.csv +0 -0
README.md
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---
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configs:
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- config_name: tags
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data_files:
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- split: train
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path: "xbrl_tags_train.csv"
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- split: test
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path: "xbrl_tags_test.csv"
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- config_name: value
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data_files:
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- split: train
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path: "value_train.csv"
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- split: test
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path: "value_test.csv"
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- config_name: formula
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data_files:
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- split: train
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path: "formula_formatted_with_tags_train.csv"
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- split: test
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path: "formula_formatted_with_tags_test.csv"
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- config_name: formula_calculations
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data_files:
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- split: train
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path: "formula_calculation_train.csv"
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- split: test
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path: "formula_calculation_test.csv"
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---
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# XBRL Extraction Dataset
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formula_calculation_test.csv
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formula_calculation_train.csv
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formula_formatted_with_tags_test.csv
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formula_formatted_with_tags_train.csv
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generate_xbrl_extract_hf_split.py
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import xml.etree.ElementTree as ET
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import re
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import json
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from typing import List, Dict
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from tqdm import tqdm
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import random
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import os.path
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from huggingface_hub import HfApi
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import csv
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random.seed(42)
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import subprocess
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example_qa_dict = {
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"xbrl_tags": {
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"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",
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"a": "us-gaap:AnExampleTagName"
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},
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"value": {
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"q": "What is the value of Exapmle company's income for the Fiscal year ending in FY 2020?",
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"a": "80000000"
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},
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"formula_calculation": {
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"q": "Can you provide the formula for Operating Profit Margin from Example Corp for the Fiscal Year ending in FY 2022?",
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"a": "(50000000 / 3590000000) * 100"
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},
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"formula_formatted_with_tags": {
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"q": "What is the formula for the Gross Profit Margin of Example Inc, formatted with the relevant US GAAP XBRL tags",
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"a": "us-gaap:ExampleTag / us-gaap:AnotherExampleTag) * 100"
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}
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}
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def get_xbrl_dataset(data: List[Dict], cat):
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"""
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Saves entries with matching category1 or category2 in the format for fine-tuning.
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Args:
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data (List[Dict]): The input JSON data.
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category (str): The category name to match.
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"""
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results = []
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for entry in data:
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question = entry["query"]
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question = re.sub(r"\(.*?\)", "", question)
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context_ids = entry["contextID"]
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# if not os.path.isfile('train/DowJones30/' + doc_path):
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# print(f"missing file {doc_path}")
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# continue
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example_qa = f"Example question: {example_qa_dict[cat]['q']}\nExample answer: {example_qa_dict[cat]['a']}"
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output = entry["raw_answer"]
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if cat == 'formula_calculation':
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question += " Answer with a formula substituted with values. "
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output = entry["value_formula_answer"]
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output = str(output)
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instruction = (f"You are a knowledgeable XBRL assistant. Your task is to analyze the XBRL context and provide an"
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f" accurate and very concise answer to the question, The example question can help you to learn the "
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f"answer format. DO NOT output xml, code, explanation or create new question. \n{example_qa}\n")
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input = f"Question: {question}\nAnswer:"
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year = int(entry["{fiscal year/quarter}"].replace("FY ", ""))
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# context_xml = add_xml(instruction + input, doc_path}, context_ids[0])
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# if len(context_xml) > 24000:
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# continue
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# print(entry["answer"])
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# entry["doc_path"], entry["answer"], entry["contextID"][0]
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results.append({
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"instruction": instruction,
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"input": input,
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"output": output,
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"year": year,
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"company": entry["ticker"],
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"doc_path": entry['doc_path'],
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"context_id": context_ids[0],
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})
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print("final length", len(results))
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return results
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def gen_xbrl(cat):
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with open("xbrl_bench_34020.json", "r") as f:
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data = json.load(f)
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filtered_data = [entry for entry in data if entry['category1'] == cat or entry['category2'] == cat]
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all_doc_path = list(set([entry['doc_path'] for entry in filtered_data]))
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print(f"Total data size for {cat}: {len(filtered_data)}, total number of filings {len(all_doc_path)}")
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random.shuffle(filtered_data)
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dataset = get_xbrl_dataset(filtered_data, cat)
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test_data = [x for x in dataset if x['year'] == 2023]
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train_data = [x for x in dataset if x['year'] != 2023]
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print(f"train size: {len(train_data)}, test size: {len(test_data)}\n")
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with open(f"{cat}_test.csv", "w", newline="") as f:
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w = csv.DictWriter(f, test_data[0].keys(), quoting=csv.QUOTE_ALL)
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w.writeheader()
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w.writerows(test_data)
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with open(f"{cat}_train.csv", "w", newline="") as f:
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w = csv.DictWriter(f, train_data[0].keys(), quoting=csv.QUOTE_ALL)
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w.writeheader()
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w.writerows(train_data)
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return train_data, test_data
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if __name__ == '__main__':
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tags_train, tags_test = gen_xbrl("xbrl_tags")
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value_train, value_test = gen_xbrl("value")
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formula_train, formula_test = gen_xbrl("formula_formatted_with_tags")
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formula_calc_train, formula_calc_test = gen_xbrl("formula_calculation")
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value_test.csv
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See raw diff
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value_train.csv
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xbrl_tags_test.csv
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See raw diff
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xbrl_tags_train.csv
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See raw diff
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