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Pass the Model to the constructor \n", " 2. Implement your domain logic in `do_run()`\n", " 1. Generate a `Prompt`. Examples for generating prompts are `ControlModel.to_instruct_prompt()`, `PromptTemplate.to_rich_prompt()` or `Prompt.from_text()`\n", + " - We recommend to not write custom `PromptTemplate`s. If necessary, look up the documentation of `PromptTemplate`\n", " 2. Run the model with the prompt\n", " 3. Map the prompt output to the task output class\n", "3. Run and test it" diff --git a/src/intelligence_layer/evaluation/evaluation/graders.py b/src/intelligence_layer/evaluation/evaluation/graders.py index cd7cf81c4..ce369e18d 100644 --- a/src/intelligence_layer/evaluation/evaluation/graders.py +++ b/src/intelligence_layer/evaluation/evaluation/graders.py @@ -22,7 +22,7 @@ def calculate_bleu(self, hypothesis: str, reference: str) -> float: reference: The baseline for the evaluation. Returns: - BLEU-score, float between 0 and 1. Where 1 means perfect match and 0 no overlap. + BLEU-score, float between 0 and 100. Where 100 means perfect match and 0 no overlap. """ bleu_score = self.bleu.corpus_score( hypotheses=[hypothesis], references=[[reference]]