System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: While commercial real estate lending may potentially be more profitable than single-family residential lending, it is generally more sensitive to regional and local economic conditions, making loss levels more difficult to predict. Collateral evaluation and financial statement analysis in these types of loans requires a more detailed analysis at the time of loan underwriting and on an ongoing basis. At December 31, 2022, we had $252.2 million of commercial real estate loans, representing 44.9% of our total loan portfolio. Commercial real estate loans typically involve higher principal amounts than other types of loans and some of our commercial borrowers have more than one loan outstanding with us. Consequently, an adverse development with respect to one loan or one credit relationship can expose us to a significantly greater risk of loss compared to an adverse development with respect to a one‑to‑four family residential loan. Repayment on these loans is dependent upon income generated, or expected to be generated, by the property securing the loan in amounts sufficient to cover operating expenses and debt service that may be adversely affected by changes in the economy or local market conditions. For example, if the cash flow from the borrower’s project is reduced as a result of leases not being obtained or renewed, the borrower’s ability to repay the loan may be impaired. Commercial real estate loans also expose a lender to greater credit risk than loans secured by one-to-four family residential real estate because the collateral securing these loans typically cannot be sold as easily as residential real estate. In addition, many of our commercial real estate loans are not fully amortizing and contain large balloon payments upon maturity. Such balloon payments may require the borrower to either sell or refinance the underlying property in order to make the payment that may increase the risk of default or non-payment.
Assistant: