Update README.md

README.md

@@ -15,8 +15,8 @@ datasets:
 # rufimelo/Legal-SBERTimbau-large
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
+Legal-SBERTimbau-large is based on Legal-BERTimbau-large whioch derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
+It is adapted to the Portuguese legal domain.
 
 ## Usage (Sentence-Transformers)
 
@@ -30,7 +30,7 @@ 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 = ["Isto é um exemplo", "Isto é um outro exemplo"]
 
 model = SentenceTransformer('rufimelo/Legal-SBERTimbau-large')
 embeddings = model.encode(sentences)
@@ -40,7 +40,7 @@ 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.
+
 
 ```python
 from transformers import AutoTokenizer, AutoModel