From 64c40d16f06534d36ca84806441879195c9bc697 Mon Sep 17 00:00:00 2001
From: Raphael Mitsch <r.mitsch@outlook.com>
Date: Wed, 11 Oct 2023 12:06:16 +0200
Subject: [PATCH] Remove binary textcat example. Format.

---
 website/docs/api/large-language-models.mdx | 37 ++--------------------
 1 file changed, 3 insertions(+), 34 deletions(-)

diff --git a/website/docs/api/large-language-models.mdx b/website/docs/api/large-language-models.mdx
index 6f4ba344e..eaa896d74 100644
--- a/website/docs/api/large-language-models.mdx
+++ b/website/docs/api/large-language-models.mdx
@@ -673,22 +673,6 @@ prompt.
 The formatting of few-shot examples is the same as those for the
 [v1](#textcat-v1) implementation.
 
-If you want to perform few-shot learning with a binary classifier, you can
-provide positive and negative examples - e. g.:
-
-```json
-[
-  {
-    "text": "You won the lottery! Wire a fee of 200$ to be able to withdraw your winnings.",
-    "answer": "Spam"
-  },
-  {
-    "text": "Your order #123456789 has arrived",
-    "answer": "NotSpam"
-  }
-]
-```
-
 #### spacy.TextCat.v2 {id="textcat-v2"}
 
 V2 includes all v1 functionality, with an improved prompt template.
@@ -718,22 +702,6 @@ V2 includes all v1 functionality, with an improved prompt template.
 The formatting of few-shot examples is the same as those for the
 [v1](#textcat-v1) implementation.
 
-If you want to perform few-shot learning with a binary classifier, you can
-provide positive and negative examples - e. g.:
-
-```json
-[
-  {
-    "text": "You won the lottery! Wire a fee of 200$ to be able to withdraw your winnings.",
-    "answer": "Spam"
-  },
-  {
-    "text": "Your order #123456789 has arrived",
-    "answer": "NotSpam"
-  }
-]
-```
-
 #### spacy.TextCat.v1 {id="textcat-v1"}
 
 Version 1 of the built-in TextCat task supports both zero-shot and few-shot
@@ -784,8 +752,9 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`.
 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
+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:
 
 ```json