From f6d9e5c4dfe6c70ecfc868906949e1a0d85d15b3 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Wed, 11 Oct 2023 12:16:40 +0200 Subject: [PATCH] Rephrase. --- website/docs/api/large-language-models.mdx | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx index eaa896d74..55d137e21 100644 --- a/website/docs/api/large-language-models.mdx +++ b/website/docs/api/large-language-models.mdx @@ -754,8 +754,9 @@ path = "textcat_examples.json" If you want to perform few-shot learning with a binary classifier (i. e. a text either should or should not be assigned to a given class), you can provide -positive and negative examples with the POS/NEG label. An example for spam -classification: +positive and negative examples with answers of "POS" or "NEG". "POS" means that +this example should be assigned the class label defined in the configuration, +"NEG" means it shouldn't. E. g. for spam classification: ```json [