Fujimori - Indo18 — Meyd-718 Bercinta Cepat Dengan Janda Sebelah Rumah Riho

So, the approach would be:

Another consideration is generating appropriate variants. For each word, we need three plausible synonyms or variations. For example, converting "Hello" to "Hi, Greetings, Hey". However, the user might expect different variants depending on context. But without knowing the actual text, it's hard to tailor the variants precisely.

Looking back at the example, "example@example.com" would be considered a name, so it remains unchanged. "123 Main St, Anytown, USA 12345" is an address, so that's a name. Then the rest of the words, if any, would be converted. However, in the provided example, there's no other text. The user included "example@example.com" and "123 Main St, Anytown, USA 12345" as placeholders. So, the approach would be: Another consideration is

Wait, but the user said "convert every word with 3 variants formatted v1." So each word in the input text (excluding names) needs to be replaced by three possible variants. The challenge is identifying which words are names and which are regular words. Without specific context, it's hard to know. If the input text includes words that could be either names or common nouns, I might have to default to treating them as regular words unless they fit a pattern of names (like capitalized words, domains, addresses, etc.).

Wait, but how do I determine if a word is a name? For example, words like "Apple" could be a fruit or a company. How do I know which is which in the input text? The user hasn't provided any specific text to work with yet. They just gave an example with the placeholder "Text:" followed by "example@example.com" and "123 Main St, Anytown, USA 12345." However, the user might expect different variants depending

Now, to apply this to the given example. Since the user provided the example of converting "hello world" to "hiearth", I can infer that each regular word is transformed, while names are kept. Therefore, if the input text includes names like email addresses or addresses, they remain the same, and other words get transformed.

1. Split the input text into words or tokens. 2. For each token, determine if it's a name (email, address, URL, capitalized proper nouns) or a regular word. 3. For regular words, generate three synonyms or variants. 4. For names, leave them unchanged. 5. Format the output with v2 for each regular word, keeping names as they are. "123 Main St, Anytown, USA 12345" is an

- Tokenize the input text into words or named entities. - For each token: - Check if it's a name (email, address, URL, proper noun). - If yes, leave it unchanged. - If not, generate three variants and format as v1. - Ensure that the output only contains the result, without explanations or additional text.