Update README.md

README.md

@@ -1,12 +1,16 @@
 ---
-library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: mit
+datasets:
+- stsb_multi_mt
+language:
+- it
+library_name: sentence-transformers
 ---
 
 # nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka
@@ -27,11 +31,16 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]
+matryoshka_dim = 64
 
-model = SentenceTransformer('nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka')
+model = SentenceTransformer('nickprock/sentence-bert-base-italian-uncased')
 embeddings = model.encode(sentences)
-print(embeddings)
+embeddings = embeddings[..., :matryoshka_dim]  # Shrink the embedding dimensions
+print(embeddings.shape)
+# => (2, 64)
+
+
 ```
 
 
@@ -52,7 +61,7 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka')