removes old references to query and doc models

examples/Semantic_text_search_using_embeddings.ipynb

@@ -1,12 +1,13 @@
 {
  "cells": [
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
  "## Semantic text search using embeddings\n",
  "\n",
- "We can search through all our reviews semantically in a very efficient manner and at very low cost, by simply embedding our search query, and then finding the most similar reviews. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
+ "We can search through all our reviews semantically in a very efficient manner and at very low cost, by embedding our search query, and then finding the most similar reviews. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
  ]
  },
  {
@@ -25,10 +26,11 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "Remember to use the documents embedding engine for documents (in this case reviews), and query embedding engine for queries. Note that here we just compare the cosine similarity of the embeddings of the query and the documents, and show top_n best matches."
+ "Here we compare the cosine similarity of the embeddings of the query and the documents, and show top_n best matches."
  ]
  },
  {