readme: ---
  language: 
  - pt
  tags:
  - generated_from_trainer
  datasets:
  - pierreguillou/lener_br_finetuning_language_model
  model-index:
  - name: checkpoints
    results:
    - task:
        name: Fill Mask
        type: fill-mask
      dataset:
        name: pierreguillou/lener_br_finetuning_language_model
        type: pierreguillou/lener_br_finetuning_language_model
      metrics:
      - name: Loss
        type: loss
        value: 1.352389
  widget:
  - text: "Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, competência que não lhe pertence, com evidente transgressão ao princípio constitucional da separação de poderes."
  ---
  
  ## (BERT base) Language modeling in the legal domain in Portuguese (LeNER-Br)
  
  **bert-base-cased-pt-lenerbr** is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the dataset [LeNER-Br language modeling](https://huggingface.co/datasets/pierreguillou/lener_br_finetuning_language_model) by using a MASK objective.
  
  You can check as well the [version large of this model](https://huggingface.co/pierreguillou/bert-large-cased-pt-lenerbr).
    
  ## Blog post
  
  This language model is used to get a NER model on the Portuguese judicial domain. You can check the fine-tuned NER model at [pierreguillou/ner-bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/ner-bert-base-cased-pt-lenerbr).
  
  All informations and links are in this blog post: [NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro](https://medium.com/@pierre_guillou/nlp-modelos-e-web-app-para-reconhecimento-de-entidade-nomeada-ner-no-dom%C3%ADnio-jur%C3%ADdico-b658db55edfb) (29/12/2021)
  
  ## Widget & APP
  
  You can test this model into the widget of this page.
  
  ## Using the model for inference in production
  ````
  # install pytorch: check https://pytorch.org/
  # !pip install transformers 
  from transformers import AutoTokenizer, AutoModelForMaskedLM
  
  tokenizer = AutoTokenizer.from_pretrained("pierreguillou/bert-base-cased-pt-lenerbr")
  model = AutoModelForMaskedLM.from_pretrained("pierreguillou/bert-base-cased-pt-lenerbr")
  ````
  
  ## Training procedure
  
  ## Notebook
  
  The notebook of finetuning ([Finetuning_language_model_BERtimbau_LeNER_Br.ipynb](https://github.com/piegu/language-models/blob/master/Finetuning_language_model_BERtimbau_LeNER_Br.ipynb)) is in github.
  
  ### Training results
  
  ````
  Num examples = 3227
  Num Epochs = 5
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 8
  Gradient Accumulation steps = 1
  Total optimization steps = 2020
  
  Step	Training Loss	Validation Loss
  100	 1.988700	     1.616412
  200	 1.724900	     1.561100
  300	 1.713400	     1.499991
  400	 1.687400	     1.451414
  500	 1.579700	     1.433665
  600	 1.556900	     1.407338
  700	 1.591400	     1.421942
  800	 1.546000	     1.406395
  900	 1.510100	     1.352389
  1000	1.507100     	1.394799
  1100	1.462200     	1.36809373471
  ````