rufimelo
/

Legal-BERTimbau-sts-large-ma-v3

@@ -26,7 +26,7 @@ metrics:
 # rufimelo/Legal-SBERTimbau-sts-large-ma
 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.
-rufimelo/Legal-SBERTimbau-sts-large-ma is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
 It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
 ## Usage (Sentence-Transformers)
@@ -43,7 +43,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
-model = SentenceTransformer('rufimelo/Legal-SBERTimbau-sts-large-ma')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -69,8 +69,8 @@ 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('rufimelo/Legal-SBERTimbau-sts-large-ma')
-model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large-ma')
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -107,7 +107,7 @@ print(sentence_embeddings)
 | paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831   |0.84575 |
 ## Training
-rufimelo/Legal-SBERTimbau-sts-large-ma is based on Legal-BERTimbau-largewhich derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
 Firstly, due to the lack of portuguese datasets, it was trained using multilingual knowledge distillation. For the Multilingual Knowledge Distillation process, the teacher model was 'sentence-transformers/stsb-roberta-large', the supposed supported language as English and the language to learn was portuguese.

 # rufimelo/Legal-SBERTimbau-sts-large-ma
 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.
+rufimelo/Legal-SBERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
 It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
 ## Usage (Sentence-Transformers)
 from sentence_transformers import SentenceTransformer
 sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
+model = SentenceTransformer('rufimelo/Legal-SBERTimbau-sts-large-ma-v3')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 sentences = ['This is an example sentence', 'Each sentence is converted']
 # Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large-ma-v3')
+model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large-ma-v3')
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 | paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831   |0.84575 |
 ## Training
+rufimelo/Legal-SBERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
 Firstly, due to the lack of portuguese datasets, it was trained using multilingual knowledge distillation. For the Multilingual Knowledge Distillation process, the teacher model was 'sentence-transformers/stsb-roberta-large', the supposed supported language as English and the language to learn was portuguese.