readme: ---
  language:
  - pt
  thumbnail: Portuguese BERT for the Legal Domain
  tags:
  - sentence-transformers
  - transformers
  - bert
  - pytorch
  - sentence-similarity
  license: mit
  pipeline_tag: sentence-similarity
  datasets:
  - stjiris/portuguese-legal-sentences-v0
  - assin
  - assin2
  - stsb_multi_mt
  - stjiris/IRIS_sts
  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."
  model-index:
  - name: BERTimbau
    results:
    - task:
        name: STS
        type: STS
      metrics:
        - name: Pearson Correlation - assin Dataset
          type: Pearson Correlation
          value: 0.7763420633772975
        - name: Pearson Correlation - assin2 Dataset
          type: Pearson Correlation
          value: 0.8067374216274927
        - name: Pearson Correlation - stsb_multi_mt pt Dataset
          type: Pearson Correlation
          value: 0.8388993109077857
        - name: Pearson Correlation - IRIS STS Dataset
          type: Pearson Correlation
          value: 0.7931353381814285
  ---
  
  
  [![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png)](https://www.inesc-id.pt/projects/PR07005/)
  
  [![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png)](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)
  
  Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/).
  
  Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)
  
  # stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v1 (Legal BERTimbau)
  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.
  stjiris/bert-large-portuguese-cased-legal-tsdae derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
  
  It was trained using the TSDAE technique with a learning rate 1e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 21.2k training steps (best performance for our semantic search system implementation)
  
  The model was presented to NLI data. 16 batch size,  2e-5 lr
  
  It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2), [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt)  and  [IRIS STS](https://huggingface.co/datasets/stjiris/IRIS_sts) datasets. 'lr': 1e-5
  
  ## 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('stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v1')
  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('stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v1')
  model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v1')
  
  # 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)
  ```
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
  )
  ```
  
  
  
  ## Citing & Authors
  
  ### Contributions
  [@rufimelo99](https://github.com/rufim