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@@ -12,7 +12,7 @@ datasets:
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  - assin2
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  ---
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- # rufimelo/Legal-SBERTimbau-large
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  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.
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  Legal-SBERTimbau-large is based on Legal-BERTimbau-large whioch derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
@@ -32,7 +32,7 @@ Then you can use the model like this:
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  from sentence_transformers import SentenceTransformer
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  sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
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- model = SentenceTransformer('rufimelo/Legal-SBERTimbau-large')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
@@ -58,8 +58,8 @@ def mean_pooling(model_output, attention_mask):
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-SBERTimbau-large')
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- model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-large}')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -106,4 +106,15 @@ SentenceTransformer(
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  ## Citing & Authors
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- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
 
 
 
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  - assin2
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  ---
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+ # rufimelo/Legal-SBERTimbau-nli-large
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  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.
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  Legal-SBERTimbau-large is based on Legal-BERTimbau-large whioch derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
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  from sentence_transformers import SentenceTransformer
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  sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
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+ model = SentenceTransformer('rufimelo/Legal-SBERTimbau-nli-large')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-SBERTimbau-nli-large')
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+ model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-nli-large}')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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  ## Citing & Authors
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+ If you use this work, please cite BERTimbau's work:
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+
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+ ```bibtex
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+ @inproceedings{souza2020bertimbau,
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+ author = {F{\'a}bio Souza and
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+ Rodrigo Nogueira and
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+ Roberto Lotufo},
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+ title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
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+ booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
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+ year = {2020}
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+ }
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+ ```