In this example, we use Jina, PyTorch, and Hugging Face transformers to build a production-ready BERT-based Financial Question Answering System. We adapt a passage reranking approach by first retrieving the top-50 candidate answers, then reranking the candidate answers using FinBERT-QA, a BERT-based model fine-tuned on the FiQA dataset that achieved the state-of-the-art results.
🦉 Please refer to this tutorial for a step-by-step guide and detailed explanations.
Motivated by the emerging demand in the financial industry for the automatic analysis of unstructured and structured data at scale, QA systems can provide lucrative and competitive advantages to companies by facilitating the decision making of financial advisers. The goal of our system is to search for a list of relevant answer passages given a question. Here is an example of a question and a ground truth answer from the FiQA dataset:
https://github.com/yuanbit/jina-financial-qa-search.git
We will use jina-financial-qa-search/ as our working directory.
pip install -r requirements.txt
bash get_data.sh
We want to index a subset of the answer passages from the FiQA dataset, dataset/test_answers.csv:
398960 From http://financial-dictionary.thefreedictionary.com/Business+Fundamentals The facts that affect a company's underlying value. Examples of business fundamentals include debt, cash flow, supply of and demand for the company's products, and so forth. For instance, if a company does not have a sufficient supply of products, it will fail. Likewise, demand for the product must remain at a certain level in order for it to be successful. Strong business fundamentals are considered essential for long-term success and stability. See also: Value Investing, Fundamental Analysis. For a stock the basic fundamentals are the second column of numbers you see on the google finance summary page, P/E ratio, div/yeild, EPS, shares, beta. For the company itself it's generally the stuff on the 'financials' link (e.g. things in the quarterly and annual report, debt, liabilities, assets, earnings, profit etc.
19183 If your sole proprietorship losses exceed all other sources of taxable income, then you have what's called a Net Operating Loss (NOL). You will have the option to "carry back" and amend a return you filed in the last 2 years where you owed tax, or you can "carry forward" the losses and decrease your taxes in a future year, up to 20 years in the future. For more information see the IRS links for NOL. Note: it's important to make sure you file the NOL correctly so I'd advise speaking with an accountant. (Especially if the loss is greater than the cost of the accountant...)
327002 To be deductible, a business expense must be both ordinary and necessary. An ordinary expense is one that is common and accepted in your trade or business. A necessary expense is one that is helpful and appropriate for your trade or business. An expense does not have to be indispensable to be considered necessary. (IRS, Deducting Business Expenses) It seems to me you'd have a hard time convincing an auditor that this is the case. Since business don't commonly own cars for the sole purpose of housing $25 computers, you'd have trouble with the "ordinary" test. And since there are lots of other ways to house a computer other than a car, "necessary" seems problematic also.
You can change the path to answer_collection.tsv to index with the full dataset.
python app.py index
At the end you will see the following:
✅ done in ⏱ 1 minute and 54 seconds 🐎 7.7/s
gateway@18904[S]:terminated
doc_indexer@18903[I]:recv ControlRequest from ctl▸doc_indexer▸⚐
doc_indexer@18903[I]:Terminating loop requested by terminate signal RequestLoopEnd()
doc_indexer@18903[I]:#sent: 56 #recv: 56 sent_size: 1.7 MB recv_size: 1.7 MB
doc_indexer@18903[I]:request loop ended, tearing down ...
doc_indexer@18903[I]:indexer size: 865 physical size: 3.1 MB
doc_indexer@18903[S]:artifacts of this executor (vecidx) is persisted to ./workspace/doc_compound_indexer-0/vecidx.bin
doc_indexer@18903[I]:indexer size: 865 physical size: 3.2 MB
doc_indexer@18903[S]:artifacts of this executor (docidx) is persisted to ./workspace/doc_compound_indexer-0/docidx.bin
We need to build a custom Executor to rerank the top-50 candidate answers. We can do this with the Jina Hub API. Let's get make sure that the Jina Hub extension is installed:
pip install "jina[hub]"
We can build the custom Ranker, FinBertQARanker by running:
jina hub build FinBertQARanker/ --pull --test-uses --timeout-ready 60000
We can now use our Financial QA search engine by running:
python app.py search
The Ranker might take some time to compute the relevancy scores since it is using a BERT-based model. You can try out this list of questions from the FiQA dataset:
• What does it mean that stocks are “memoryless”?
• What would a stock be worth if dividends did not exist?
• What are the risks of Dividend-yielding stocks?
• Why do financial institutions charge so much to convert currency?
• Is there a candlestick pattern that guarantees any kind of future profit?
• 15 year mortgage vs 30 year paid off in 15
• Why is it rational to pay out a dividend?
• Why do companies have a fiscal year different from the calendar year?
• What should I look at before investing in a start-up?
• Where do large corporations store their massive amounts of cash?
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