Update README.md

README.md

@@ -8,4 +8,25 @@ library_name: sentence-transformers
 pipeline_tag: text-classification
 tags:
 - cross-encoder
----
+---
+
+# Cross-Encoder for STSB-Multi
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
+The original model is [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased).
+
+## Training Data
+This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark), in particular the italian translation. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 
+
+
+## Usage and Performance
+
+Pre-trained models can be used like this:
+```
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('model_name')
+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')`.
+
+You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class