From d9a6465b7b99854ca08f4c74e90fc62bcdcdd2da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Christopher=20Sch=C3=B6lzel?= Date: Sat, 23 Mar 2024 00:13:16 +0100 Subject: [PATCH] adjusts wording --- exercises/2024/ChatGPT/ngram/ngram.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/exercises/2024/ChatGPT/ngram/ngram.md b/exercises/2024/ChatGPT/ngram/ngram.md index 7c0c259..836cc18 100644 --- a/exercises/2024/ChatGPT/ngram/ngram.md +++ b/exercises/2024/ChatGPT/ngram/ngram.md @@ -17,7 +17,7 @@ Create a class `NgramModel` in `ngram.py` that can generate an ngram model and m 1. `__init__(self, n: int)` - Initialize the ngram model with the value of `n`, the order of the ngrams. 2. `train(self, text: str)` - Train the model using the provided text. This method should build the ngram model. -3. `predict(self, prefix: str) -> List[str]` - Given a prefix, predict the next word based on the trained ngram model. Return a list of possible predictions sorted by their probabilities. +3. `predict(self, prefix: str) -> List[str]` - Given a prefix, predict the next word based on the trained ngram model. Return a list of up to 10 possible predictions sorted by their probabilities (descending). Example: