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: We intend to continue our focus on prudently growing our commercial real estate and commercial loan portfolio. At December 31, 2023, our commercial real estate loan portfolio was $2.34 billion, or 49.3% of total loans, and our commercial and industrial loan portfolio was $466.4 million, or 9.8% of total loans. Given their larger balances and the complexity of the underlying collateral, commercial real estate and commercial loans generally expose a lender to greater credit risk than loans secured by owner-occupied one- to four-family real estate. Also, many of our borrowers or related groups of borrowers have more than one of these types of loans outstanding. Consequently, an adverse development with respect to one loan or one credit relationship can expose us to significantly greater risk of loss compared to an adverse development with respect to a one- to four-family residential real estate loan. Payments on loans secured by commercial properties are often dependent on the income produced by the underlying properties which, in turn, depends on the successful operation and management of the properties and the businesses that operate within them. Accordingly, repayment of these loans is subject to conditions in the real estate market or the local economy. Additionally, the COVID-19 pandemic has had a potentially long-term negative impact on certain commercial real estate assets due to the risk that tenants may reduce the office space they lease as some portion of the workforce continues to work remotely on a hybrid or fulltime basis. If loans that are collateralized by real estate or other business assets become troubled and the value of the collateral has been significantly impaired, then we may not be able to recover the full contractual amount of principal and interest that we anticipated at the time we originated the loan, which could cause us to increase our provision for credit losses on loans which would in turn adversely affect our operating results and financial condition. Further, if we foreclose on the collateral, our holding period for the collateral may be longer than for one- to four-family real estate loans because there are fewer potential purchasers of the collateral, which can result in substantial holding costs.
Assistant: