Update README.md

README.md

@@ -1,18 +1,24 @@
 ---
-library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: mit
+datasets:
+- stsb_multi_mt
+- unicamp-dl/mmarco
+language:
+- it
+library_name: sentence-transformers
 ---
 
-# {MODEL_NAME}
+# {multi-sentence-BERTino}
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
+This model is trained from  [indigo-ai/BERTino](https://huggingface.co/indigo-ai/BERTino) using [mmarco italian](https://huggingface.co/datasets/unicamp-dl/mmarco) (200K) and [stsb italian](https://huggingface.co/datasets/stsb_multi_mt).
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
@@ -27,11 +33,12 @@ 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."]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('nickprock/multi-sentence-BERTino')
 embeddings = model.encode(sentences)
 print(embeddings)
+
 ```
 
 
@@ -52,11 +59,11 @@ 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('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('nickprock/multi-sentence-BERTino')
+model = AutoModel.from_pretrained('nickprock/multi-sentence-BERTino')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -70,6 +77,7 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 
 print("Sentence embeddings:")
 print(sentence_embeddings)
+
 ```