diff --git a/.gitignore b/.gitignore
index 10ad02ef32c99c292a43aaec5bd41314cb7508ee..2f0ed0ea4e99e44b86960625170d2b69e1686379 100644
--- a/.gitignore
+++ b/.gitignore
@@ -154,4 +154,6 @@ subset*
 time*
 
 
-/data*
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+/data*
+
+output_bert_allcooc_adjsampling3radius20km_batch32_epoch10
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diff --git a/README.md b/README.md
index 37971368cc954b9404d08645a02e035c2dbd3ca6..9bd2998e60b556fbdefd92d5a4ab04e033855a70 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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