Updated README

README.md

@@ -1 +1,34 @@
-hello
+---
+pipeline_tag: text-classification
+language: 
+  - it
+datasets:
+- stsb_multi_mt
+tags:
+- cross-encoder
+- sentence-similarity
+- transformers
+---
+
+# Cross-Encoder
+
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
+
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br>
+    Marco Lodola, Monument to Umberto Eco, Alessandria 2019
+</p>
+
+## Training Data
+This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 
+
+
+## Usage and Performance
+
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
+scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
+```
+
+The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.