Update README.md

README.md

@@ -5,7 +5,12 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: apache-2.0
+datasets:
+- stsb_multi_mt
+language:
+- it
+library_name: sentence-transformers
 ---
 
 # {MODEL_NAME}
@@ -28,7 +33,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('nickprock/stsbm-sentence-flare-it')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -54,8 +59,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('nickprock/stsbm-sentence-flare-it')
+model = AutoModel.from_pretrained('nickprock/stsbm-sentence-flare-it')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -123,4 +128,4 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+Other information on [flare-it page](https://huggingface.co/osiria/flare-it)