From 455bafc802d16c0e307ada1e8f967bd7c732c353 Mon Sep 17 00:00:00 2001
From: Rosie Wood <rwood@turing.ac.uk>
Date: Tue, 1 Oct 2024 12:16:07 +0100
Subject: [PATCH] change is to was, remove now

---
 .../introduction-to-mapreader/what-is-mapreader.rst       | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/source/introduction-to-mapreader/what-is-mapreader.rst b/docs/source/introduction-to-mapreader/what-is-mapreader.rst
index 845461f6..9b917477 100644
--- a/docs/source/introduction-to-mapreader/what-is-mapreader.rst
+++ b/docs/source/introduction-to-mapreader/what-is-mapreader.rst
@@ -13,9 +13,9 @@ MapReader was developed in the `Living with Machines <https://livingwithmachines
 Origin of MapReader
 -------------------
 
-MapReader is a groundbreaking interdisciplinary tool that emerged from a specific set of geospatial historical research questions.
-The classification pipeline was inspired by methods in biomedical imaging and geographic information science, which were adapted for use by historians, for example in our `Journal of Victorian Culture <https://doi.org/10.1093/jvcult/vcab009>`__ and `Geospatial Humanities 2022 SIGSPATIAL workshop <https://arxiv.org/abs/2111.15592>`__ papers.
-The success of the tool subsequently generated interest from plant phenotype researchers working with large image datasets, and so MapReader is an example of cross-pollination between the humanities and the sciences made possible by reproducible data science.
+MapReader was a groundbreaking interdisciplinary tool that emerged from a specific set of geospatial historical research questions.
+The classification pipeline was inspired by methods in biomedical imaging and geographic information science, which were adapted for use by historians - for example in our `Journal of Victorian Culture <https://doi.org/10.1093/jvcult/vcab009>`__ and `Geospatial Humanities 2022 SIGSPATIAL workshop <https://arxiv.org/abs/2111.15592>`__ papers.
+The success of the tool subsequently generated interest from plant phenotype researchers working with large image datasets and so MapReader is an example of cross-pollination between the humanities and the sciences made possible by reproducible data science.
 
 Since then, MapReader has expanded to include a text spotting pipeline, which enables users to detect and recognize text in map images.
 
@@ -33,7 +33,7 @@ This unique way of pre-processing map images enables the use of image classifica
 What is 'the MapReader pipeline'?
 ---------------------------------
 
-MapReader now contains two different pipelines:
+MapReader contains two different pipelines:
 
 - Classification pipeline: This pipeline enables users to fine-tune a classification model and predict the labels of patches created from a parent image.
 - Text spotting pipeline: This pipeline enables users to detect and recognize text in map images.