readme: 
  ---
  language: 
    - pt
  thumbnail: "Portuguese BERT for the Legal Domain"
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - sentence-similarity
  - transformers
  datasets:
  - assin
  - assin2
  - stsb_multi_mt
  - rufimelo/PortugueseLegalSentences-v1
  widget:
  - source_sentence: "O advogado apresentou as provas ao juíz."
    sentences:
      - "O juíz leu as provas."
      - "O juíz leu o recurso."
      - "O juíz atirou uma pedra."
    example_title: "Example 1"
  model-index:
  - name: BERTimbau
    results:
    - task:
        name: STS
        type: STS
      metrics:
        - name: Pearson Correlation - assin Dataset
          type: Pearson Correlation
          value: 0.81326
        - name: Pearson Correlation - assin2 Dataset
          type: Pearson Correlation
          value: 0.83130
        - name: Pearson Correlation - stsb_multi_mt pt Dataset
          type: Pearson Correlation
          value: 0.786314
  ---
  
  # rufimelo/Legal-BERTimbau-large-TSDAE-sts-v2
  
  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-BERTimbau-large-TSDAE-sts-v2 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
  It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  pip install -U sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
  
  model = SentenceTransformer('rufimelo/Legal-BERTimbau-large-TSDAE-sts-v2')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  
  ## Usage (HuggingFace Transformers)
  
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  import torch
  
  
  #Mean Pooling - Take attention mask into account for correct averaging
  def mean_pooling(model_output, attention_mask):
      token_embeddings = model_output[0] #First element of model_output contains all token embeddings
      input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
      return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
  
  
  # Sentences we want sentence embeddings for
  sentences = ['This is an example sentence', 'Each sentence is converted']
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE-sts-v2')
  model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE-sts-v2')
  
  # Tokenize sentences
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
  
  # Compute token embeddings
  with torch.no_grad():
      model_output = model(**encoded_input)
  
  # Perform pooling. In this case, mean pooling.
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  
  ## Evaluation Results STS
  
  
  | Model| Assin | Assin2|stsb_multi_mt pt| avg|
  | ---------------------------------------- | ---------- | ---------- |---------- |---------- |
  | Legal-BERTimbau-sts-base| 0.71457| 0.73545 | 0.72383|0.72462|
  | Legal-BERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |0.78886|
  | Legal-BERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|0.79307|
  | Legal-BERTimbau-base-TSDAE-sts|0.78814 |0.81380 |0.75777|0.78657|
  | Legal-BERTimbau-sts-large| 0.76629| 0.82357 | 0.79120|0.79369|
  | Legal-BERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |0.79715|
  | Legal-BERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|0.80142|
  | Legal-BERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 0.81863|
  | Legal-BERTimbau-sts-large-ma-v3| 0.7749| **0.8470**| 0.8364| **0.81943**|
  | Legal-BERTimbau-large-v2-sts| 0.71665| 0.80106| 0.73724| 0.75165|
  | Legal-BERTimbau-large-TSDAE-sts| 0.72376| 0.79261| 0.73635| 0.75090|
  | Legal-BERTimbau-large-TSDAE-sts-v2| 0.81326| 0.83130| 0.786314| 0.81029|
  | Legal-BERTimbau-large-TSDAE-sts-v3|0.80703 |0.82270 |0.77638 |0.80204 |
  | ---------------------------------------- | ---------- |---------- |---------- |---------- |
  | BERTimbau base Fine-tuned for STS|**0.78455** | 0.80626|0.82841|0.80640|
  | BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|0.81245|
  | ---------------------------------------- | ---------- |---------- |---------- |---------- |
  | paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |0.78429|
  | paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831   |**0.84575**|0.80682|
  
  ## Training
  
  rufimelo/Legal-BERTimbau-large-TSDAE-sts-v2 is based on rufimelo/Legal-BERTimbau-large-TSDAE-sts-v2 which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) large.
  
  rufimelo/Legal-BERTimbau-large-TSDA