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 subject to a number of risks associated with our indebtedness, including: 1) we must dedicate a portion of our cash flows from operations to pay debt service costs, and therefore we have less funds available for operations and other purposes; 2) it may be more difficult and expensive to obtain additional funds through financings, if available at all; 3) we are more vulnerable to economic downturns and fluctuations in interest rates, less able to withstand competitive pressures and less flexible in reacting to changes in our industry and general economic conditions; and 4) if we default under any of our existing credit facilities or if our creditors demand payment of a portion or all of our indebtedness, we may not have sufficient funds to make such payments. As of December 31, 2022, our outstanding current liabilities were approximately $5.8 million, which consisted primarily of short-term bank loans and accrued expenses. On April 5 and 20, 2020, we entered into certain exchange agreements separately with certain U.S. and non-U.S. holders of certain convertible promissory notes in the aggregate amount of $1,446,780; pursuant to the exchange agreements, we issued to the Holders an aggregate of 795,735 shares of Common Stock and warrants to purchase 795,735 shares of Common Stock. On November 9, 2020, we entered into an exchange agreement with a certain non-U.S. holder of certain convertible promissory notes in the amount of $270,272; pursuant to the exchange agreements, we will issue to the holder an aggregate of 120,121 shares of Common Stock and warrants to purchase 120,121 shares of Common Stock. We also agreed to issue an aggregate of 545,182 options of common stock to some of our employees in lieu of their deferred salaries in an aggregate amount of $1,090,360.
Assistant: