So, the key challenges are correctly identifying names and finding accurate synonyms. Since the user wants the result only, after processing, the model should output the transformed text with synonyms in the specified format, keeping names unchanged.
1. Split the input text into words. 2. For each word, check if it's a proper noun (capitalized). 3. If it's a proper noun, leave it. 4. If not, find three synonyms. 5. Format each with syn2. 6. Combine the words back into the output text. Kamera 10 vjecare Masturbon ne karrige Vajza Pe...
Another thing: Some words might not have three synonyms. For example, "jumps" could be replaced with "leaps, springs, bounds." But if the word is less common, finding three might be challenging. In that case, use the best available options. So, the key challenges are correctly identifying names
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Potential issues: Words that are names but look like common nouns. For example, "Apple" could be a company name or a fruit. Without context, it's hard to tell. However, the user wants names kept, so if it's a known name, it stays. Otherwise, replace with synonyms. So maybe rely on capitalization, but that's not foolproof. Split the input text into words
Also, ensuring that the output is only the modified text without any extra explanation. So the model needs to process each word systematically, check for names, and apply synonyms where possible. Let me outline the steps again:
Testing with a sample input would help. Let's take "The Amazon is a big river." Here, "Amazon" is a name (proper noun), so kept. "The," "a" are articles, replaced with synonyms if possible. "Big" becomes huge, "river" becomes canal? Wait, "canal" is not a synonym for river. Maybe waterway is better. Need to be careful with the synonym accuracy.