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 are not required to observe specific diversification criteria. Therefore, our investments may be concentrated in certain property types that are subject to higher risk of foreclosure or secured by properties concentrated in a limited number of geographic locations. For example, as of December 31, 2020, approximately 55.7% and 53.0% of the loans in our loan portfolio, based on total loan commitments and unpaid principal balance, respectively, consisted of loans secured by office buildings and approximately 14.9% and 16.2% of the loans in our loan portfolio, based on total loan commitments and unpaid principal balance, respectively, consisted of loans secured by hotels. Although we attempt to mitigate our risk through various credit and structural protections, we cannot assure you that these efforts will be successful. To the extent that our portfolio is concentrated in any one region or type of asset, downturns relating generally to such region or type of asset may result in defaults on a number of our investments within a short time period, which may reduce our net income and, depending upon whether such loans are matched-term funded, may pressure our liquidity position. While we seek to construct our portfolio to mitigate such risk, we may not be successful and this may be beyond our control, such as due to underlying loan repayments concentrated in a particular property type. Such outcomes may adversely affect the market price of our common stock and, accordingly, have a material adverse effect on us. For more information on the concentration of credit risk in our loan portfolio by geographic region, property type and loan category, see Note 16 to our Consolidated Financial Statements included in this Form 10-K.
Assistant: