But how to distinguish between common and proper nouns without more context? Maybe based on capitalization and presence in a name database. Since I can't access external databases, I'll have to make educated guesses. For example, "Amazon" is a proper noun, so it remains; "river" is a common noun, so replace with synonyms.
Let's take the example sentence. "The" is an article; names here are "fox" and "dog" (common nouns, not names). So "quick" would be replaced with nimble, "brown" with ochre, etc. But I need to be careful not to replace any proper nouns. For instance, if there's a name like "John," it stays as is. Kamera 10 vjecare Masturbon ne karrige Vajza Pe...
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: But how to distinguish between common and proper
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. For example, "Amazon" is a proper noun, so