Update model metadata

README.md

@@ -1,5 +1,16 @@
 ---
 license: apache-2.0
+datasets:
+- sentence-transformers/msmarco
+language:
+- en
+- de
+base_model:
+- microsoft/Multilingual-MiniLM-L12-H384
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+tags:
+- transformers
 ---
 # Cross-Encoder for MS MARCO - EN-DE
 
@@ -15,7 +26,9 @@ The training code is available in this repository, see `train_script.py`.
 When you have [SentenceTransformers](https://www.sbert.net/) installed, you can use the model like this:
 ```python
 from sentence_transformers import CrossEncoder
+
 model = CrossEncoder('model_name', max_length=512)
+
 query = 'How many people live in Berlin?'
 docs = ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.']
 pairs = [(query, doc) for doc in docs]
@@ -69,4 +82,4 @@ We also check the performance of bi-encoders using the same evaluation: The retr
 
 
 
- Note: Docs / Sec gives the number of (query, document) pairs we can re-rank within a second on a V100 GPU.
+ Note: Docs / Sec gives the number of (query, document) pairs we can re-rank within a second on a V100 GPU.