So, the user provided an example input sentence. Let me imagine that. For each word that isn't a name, I need to find three synonyms. Names should stay the same. The output should replace each such word with the three synonyms in the specified format. The example given in the history shows how it's done. For instance, "Apple Inc. unveiled its newest iPhone model, the iPhone 11, which comes equipped with an A13 Bionic chip and features improved dual-lens cameras." becomes the transformed text with synonyms.
But the example provided in the history has "dual-lens cameras" as a phrase. In the original sentence, "dual-lens" is part of the name? Probably not. In the example, "dual-lens" is describing the cameras, so it's modified by "improved," but the cameras are the name? No. So in that case, "dual-lens" would be replaced with three synonyms. However, in the example, "dual-lens" becomes syn1? Wait, in the example provided earlier, the transformed sentence has "dual-lens" replaced with something like syn2? Let me check: Original example's transformed sentence includes "wide-angle" for "dual-lens". So that's correct. pirater mot de passe facebook gratuit sans payer
Wait, the user's instruction says to rework all words with three alternatives, but they also mention "keep names intact." Names like "Apple Inc.", "iPhone", "iPhone 11", "A13 Bionic", etc., should not be altered. So the task is to go through each word in the input, check if it's part of a name (like proper nouns or brand names), and if not, replace it with syn1 format. So, the user provided an example input sentence
1. Tokenize the input sentence into words/compound terms. 2. Identify which tokens are names (proper nouns, brand names, model numbers, etc.). 3. For each non-name token, find three synonyms. 4. Replace each non-name token with the syn2 format. 5. Output the result without extra text or explanations. Names should stay the same
I need to process each word in the sentence and determine if it's part of a name. If it is, leave it. If not, find three synonyms. But how to do that programmatically? Well, in this context, the user is probably using a text-to-text model that can infer the context and apply the substitutions accordingly. So as a human, I can look at each word and decide if it's a name. For example, "iPhone 11" is a specific model, so that's a name. "A13 Bionic chip" is a specific chip, so that's a name. The company name "Apple Inc." is also a name.
Another thing to note: the example uses the syn3 format, separated by pipes. So the output should have each replaceable word in that format, with three options. Also, the user wants only the result, so no extra text or explanations. Just the transformed sentence.