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: Our business depends on our ability to successfully measure and manage credit risk. As a lender, we are exposed to the risk that the principal of, or interest on, a loan will not be paid timely or at all or that the value of any collateral supporting a loan will be insufficient to cover our outstanding exposure. In addition, we are exposed to risks with respect to the period of time over which the loan may be repaid, risks relating to proper loan underwriting, risks resulting from changes in economic and industry conditions, and risks inherent in dealing with individual loans and borrowers. The creditworthiness of a borrower is affected by many factors, including local market conditions and general economic conditions. Many of our loans are made to small to medium-sized businesses that are less able to withstand competitive, economic and financial pressures than larger borrowers. If the overall economic climate in the United States, generally, or in our market specifically, experiences material disruption, our borrowers may experience difficulties in repaying their loans, the collateral we hold may decrease in value or become illiquid, and the level of nonperforming loans, charge-offs and delinquencies could rise and require significant additional provisions for credit losses. Additional factors related to the credit quality of multifamily residential, real estate construction and other commercial real estate loans include the quality of management of the business and tenant vacancy rates.
Assistant: