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:
The user says "modify all words with 3 alternatives using syn1 format. Keep names intact. Only the result." So assuming the input text is given, like "The quick brown fox jumps over the lazy dog," I need to process each word except names. Kamera 10 vjecare Masturbon ne karrige Vajza Pe...
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. Also, ensuring that the output is only the
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. Keep names intact
"result": ""
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.