Update README.md

README.md

@@ -5,10 +5,15 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: mit
+datasets:
+- unicamp-dl/mmarco
+language:
+- it
+library_name: sentence-transformers
 ---
 
-# {MODEL_NAME}
+# MMARCO-bert-base-italian-uncased
 
 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.
 
@@ -26,14 +31,13 @@ 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/sentence-bert-base-italian-uncased')
 embeddings = model.encode(sentences)
 print(embeddings)
-```
-
 
+```
 
 ## Usage (HuggingFace Transformers)
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
@@ -51,11 +55,11 @@ 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/sentence-bert-base-italian-uncased')
+model = AutoModel.from_pretrained('nickprock/sentence-bert-base-italian-uncased')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -69,6 +73,7 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 
 print("Sentence embeddings:")
 print(sentence_embeddings)
+
 ```