From 33651e79d5ec6c03199db5d1d8989dd1842ad00c Mon Sep 17 00:00:00 2001
From: Jacques Fize <jacques.fize@insa-lyon.fr>
Date: Fri, 14 Feb 2020 16:16:52 +0100
Subject: [PATCH] Change README
---
README.md | 26 ++++++++++++--------------
1 file changed, 12 insertions(+), 14 deletions(-)
diff --git a/README.md b/README.md
index 6bdd557..b43d729 100644
--- a/README.md
+++ b/README.md
@@ -26,24 +26,24 @@ For Anaconda users
while read requirement; do conda install --yes $requirement; done < requirements.txt
-
<hr>
## Prepare required data
### Geonames data
- * download the Geonames data use to train the network [here](download.geonames.org/export/dump/)
- * download the hierarchy data [here](http://download.geonames.org/export/dump/hierarchy.zip)
- * unzip both file in the directory of your choice
- * run the script `train_test_split_geonames.py <geoname_filename>`
+ 1. Download the Geonames data use to train the network [here](download.geonames.org/export/dump/)
+ 2. download the hierarchy data [here](http://download.geonames.org/export/dump/hierarchy.zip)
+ 3. unzip both file in the directory of your choice
+ 4. run the script `train_test_split_geonames.py <geoname_filename>`
### Cooccurence data
- * First, you must download the Wikipedia corpus from which you want to extract co-occurrences : [English Wikipedia Corpus](https://dumps.wikimedia.org/enwiki/20200201/enwiki-20200201-pages-articles.xml.bz2)
- * Parse the corpus with Gensim script using the following command : `python3 -m gensim.scripts.segment_wiki -i -f <wikicorpus> -o <1stoutputname>.json.gz`
- * Build a page of interest file that contains a list of Wikipedia pages. The file must be a csv with the following column : title,latitude,longitude.<br> You can find [here](https://projet.liris.cnrs.fr/hextgeo/files/place_en_fr_page_clean.csv) a page of interest file that contains places that appears in both FR and EN wikipedia.
- * Then using and index that contains pages of interest run the command : `python3 script/get_cooccurrence.py <page_of_interest_file> <2noutputname> -c <1stoutputname>.json.gz`
- * Finally, split the resulting dataset with the script `train_test_split_cooccurrence_data.py <2ndoutputname>`
+ 5. First, you must download the Wikipedia corpus from which you want to extract co-occurrences : [English Wikipedia Corpus](https://dumps.wikimedia.org/enwiki/20200201/enwiki-20200201-pages-articles.xml.bz2)
+ 6. Parse the corpus with Gensim script using the following command : `python3 -m gensim.scripts.segment_wiki -i -f <wikicorpus> -o <1stoutputname>.json.gz`
+ 7. Build a page of interest file that contains a list of Wikipedia pages. The file must be a csv with the following column : title,latitude,longitude.<br> You can find [here](https://projet.liris.cnrs.fr/hextgeo/files/place_en_fr_page_clean.csv) a page of interest file that contains places that appears in both FR and EN wikipedia.
+ 8. Then using and index that contains pages of interest run the command : `python3 script/get_cooccurrence.py <page_of_interest_file> <2noutputname> -c <1stoutputname>.json.gz`
+ 9. Finally, split the resulting dataset with the script `train_test_split_cooccurrence_data.py <2ndoutputname>`
+
### If you're in a hurry
French Geonames, French Wikipedia cooccurence data, and their train/test splits datasets can be found here : [https://projet.liris.cnrs.fr/hextgeo/files/](https://projet.liris.cnrs.fr/hextgeo/files/)
@@ -52,11 +52,9 @@ French Geonames, French Wikipedia cooccurence data, and their train/test splits
## Train the network
-The script `combination_embeddings.py` is the one responsible of the neural network training
-
To train the network with default parameter use the following command :
- python3 combination_embeddings.py -a -i <geoname data filename> <hierarchy geonames data filename>
+ python3 combination_embeddings.py -i <geoname data filename> <hierarchy geonames data filename>
### Available parameters
@@ -73,4 +71,4 @@ To train the network with default parameter use the following command :
| -t,--tolerance-value | K-value in the computation of the accuracy@k |
| -e,--epochs | number of epochs |
| -d,--dimension | size of the ngram embeddings |
-| --admin_code_1 | (Optional) If you wish to train the network on a specificate region |
+| --admin_code_1 | (Optional) If you wish to train the network on a specific region |
--
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