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 on the dataset LeNER-Br language modeling by using a MASK objective.
You can check as well the version large of this model.
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.
All informations and links are in this blog post: NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro (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) 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