From 6fe5e31cd115b634a08f124d5098f2d615724b43 Mon Sep 17 00:00:00 2001
From: Jacques Fize <jacques.fize@insa-lyon.fr>
Date: Mon, 7 Sep 2020 12:14:59 +0200
Subject: [PATCH] Change Readme (add Bert)

---
 .gitignore |  4 +++-
 README.md  | 11 +++++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 10ad02e..2f0ed0e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -154,4 +154,6 @@ subset*
 time*
 
 
-/data*
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+/data*
+
+output_bert_allcooc_adjsampling3radius20km_batch32_epoch10
\ No newline at end of file
diff --git a/README.md b/README.md
index 3797136..9bd2998 100644
--- a/README.md
+++ b/README.md
@@ -93,3 +93,14 @@ grid.run()
 | -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 specific region             |
+
+
+# New model based on BERT embeddings
+
+In the recent years, BERT architecture proposed by Google researches enables to outperform state-of-art methods for differents tasks in NLP (POS, NER, Classification). To verify if BERT embeddings would permit to increase the performance of our approach, we code a script to use bert with our data. In our previous model, the model returned two values each on between [0,1]. Using Bert, the task has shifted to classification (softmax) where each class correspond to a cell on the glob. We use the hierarchical projection model : Healpix. Other projections model like S2geometry can be considered : https://s2geometry.io/about/overview.
+
+In order, to run this model training, run the `bert.py` script :
+
+    python3 bert.py <train_dataset> <test_dataset>
+
+The train and test dataset are table data composed of two columns: sentence and label.
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-- 
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