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
  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: 0.77274424197425
        - name: Pearson Correlation - assin2 Dataset
          type: Pearson Correlation
          value: 0.8121493150774828
        - name: Pearson Correlation - stsb_multi_mt pt Dataset
          type: Pearson Correlation
          value: 0.8499768588704257
        - name: Pearson Correlation - IRIS sts Dataset
          type: Pearson Correlation
          value: 0.8499768588704257
  ---
  
  [![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-mlm-mkd-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-mlm-mkd-nli-sts-v1 derives from stjiris/bert-large-portuguese-cased-legal-mlm (legal variant of [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large).
  
  It was trained using the MLM technique with a learning rate 1e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 15000 training steps (best performance for our semantic search system implementation)
  
  This model was subjected to Multilingual Knowledge Distillation technique (mkd). 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
  The dataset used was: TED 2020 – Parallel Sentences Corpus. TED 2020 contains around 4000 TED and TED-X transcripts from July 2020. These transcripts were translated by volunteers into more than 100 languages, adding up to a total of 10 544 174 sentences. 
  
  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-mlm-mkd-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-mlm-mkd-nli-sts-v1')
  model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-mkd-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, encod