Update README.md

README.md

@@ -8,6 +8,21 @@ This model was trained on the [MS Marco Passage Ranking](https://github.com/micr
 The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
 
 
+## Usage with SentenceTransformers
+
+The usage is easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then you can use the pre-trained models like this:
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder('cross-encoder/ms-marco-electra-base')
+scores = model.predict([
+    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
+    ("How many people live in Berlin?", "Berlin is well known for its museums."),
+])
+print(scores)
+# [9.9227107e-01 2.0136760e-05]
+```
+
 ## Usage with Transformers
 
 ```python
@@ -26,16 +41,6 @@ with torch.no_grad():
 ```
 
 
-## Usage with SentenceTransformers
-
-The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
-```python
-from sentence_transformers import CrossEncoder
-model = CrossEncoder('model_name', max_length=512)
-scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
-```
-
-
 ## Performance
 In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.