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(BERT base) NER model in the legal domain in Portuguese

README under construction

ner-legal-bert-base-cased-ptbr is a NER model (token classification) in the legal domain in Portuguese that was finetuned from the model dominguesm/legal-bert-base-cased-ptbr by using a NER objective.

The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese (more information below) were used with the following labels:

  • PESSOA
  • ORGANIZACAO
  • LOCAL
  • TEMPO
  • LEGISLACAO
  • JURISPRUDENCIA

The labels were inspired by the LeNER_br dataset.

Training Dataset

The dataset of ner-legal-bert-base-cased-ptbr include:

  • 971932 examples of miscellaneous legal documents (train split)
  • 53996 examples of miscellaneous legal documents (valid split)
  • 53997 examples of miscellaneous legal documents (test split)

The data used was provided by the BRAZILIAN SUPREME FEDERAL TRIBUNAL, through the terms of use: LREC 2020.

The results of this project do not imply in any way the position of the BRAZILIAN SUPREME FEDERAL TRIBUNAL, all being the sole and exclusive responsibility of the author of the model.

Using the model for inference in production

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

# parameters
model_name = "dominguesm/ner-legal-bert-base-cased-ptbr"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

input_text = "Acrescento que não há de se falar em violação do artigo 114, § 3º, da Constituição Federal, posto que referido dispositivo revela-se impertinente, tratando da possibilidade de ajuizamento de dissídio coletivo pelo Ministério Público do Trabalho nos casos de greve em atividade essencial."

# tokenization
inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt")
tokens = inputs.tokens()

# get predictions
outputs = model(**inputs).logits
predictions = torch.argmax(outputs, dim=2)

# print predictions
for token, prediction in zip(tokens, predictions[0].numpy()):
    print((token, model.config.id2label[prediction]))

You can use pipeline, too. However, it seems to have an issue regarding to the max_length of the input sequence.

from transformers import pipeline

model_name = "dominguesm/ner-legal-bert-base-cased-ptbr"

ner = pipeline(
    "ner",
    model=model_name
) 

ner(input_text, aggregation_strategy="average")

Training procedure

Hyperparameters

batch, learning rate...

  • per_device_batch_size = 64
  • gradient_accumulation_steps = 2
  • learning_rate = 2e-5
  • num_train_epochs = 3
  • weight_decay = 0.01
  • optimizer = torch.optim.AdamW
  • epsilon = 1e-08
  • lr_scheduler_type = linear

save model & load best model

  • save_total_limit = 3
  • logging_steps = 1000
  • eval_steps = logging_steps
  • evaluation_strategy = 'steps'
  • logging_strategy = 'steps'
  • save_strategy = 'steps'
  • save_steps = logging_steps
  • load_best_model_at_end = True
  • fp16 = True

Training results

Num examples = 971932
Num Epochs = 3
Instantaneous batch size per device = 64
Total train batch size (w. parallel, distributed & accumulation) = 128
Gradient Accumulation steps = 2
Total optimization steps = 22779
Evaluation Infos:
  Num examples = 53996
  Batch size = 128
Step Training Loss Validation Loss Precision Recall F1 Accuracy
1000 0.113900 0.057008 0.898600 0.938444 0.918090
2000 0.052800 0.048254 0.917243 0.941188 0.929062
3000 0.046200 0.043833 0.919706 0.948411 0.933838
4000 0.043500 0.039796 0.928439 0.947058 0.937656
5000 0.041400 0.039421 0.926103 0.952857 0.939290
6000 0.039700 0.038599 0.922376 0.956257 0.939011
7000 0.037800 0.036463 0.935125 0.950937 0.942964
8000 0.035900 0.035706 0.934638 0.954147 0.944292
9000 0.033800 0.034518 0.940354 0.951991 0.946136
10000 0.033600 0.033454 0.938170 0.956097 0.947049
11000 0.032700 0.032899 0.934130 0.959491 0.946641
12000 0.032200 0.032477 0.937400 0.959150 0.948151
13000 0.031200 0.033207 0.937058 0.960506 0.948637
14000 0.031400 0.031711 0.938765 0.959711 0.949123
15000 0.030600 0.031519 0.940488 0.959413 0.949856
16000 0.028500 0.031618 0.943643 0.957693 0.950616
17000 0.028000 0.031106 0.941109 0.960687 0.950797
18000 0.027800 0.030712 0.942821 0.960528 0.951592
19000 0.027500 0.030523 0.942950 0.960947 0.951864
20000 0.027400 0.030577 0.942462 0.961754 0.952010
21000 0.027000 0.030025 0.944483 0.960497 0.952422
22000 0.026800 0.030162 0.943868 0.961418 0.952562

Validation metrics by Named Entity (Test Dataset)

  • Num examples = 53997
  • overall_precision: 0.9432396865925381
  • overall_recall: 0.9614334116769161
  • overall_f1: 0.9522496545298874
  • overall_accuracy': 0.9894741602608071
Label Precision Recall F1 Accuracy Entity Examples
JURISPRUDENCIA 0.8795197115548148 0.9037275221501844 0.8914593047810311 57223
LEGISLACAO 0.9405395935529082 0.9514071028567378 0.9459421362370934 84642
LOCAL 0.9011495452253004 0.9132358124779697 0.9071524233856495 56740
ORGANIZACAO 0.9239028155165304 0.954964947845235 0.9391771163875446 183013
PESSOA 0.9651685220572037 0.9738545198908279 0.9694920661875761 193456
TEMPO 0.973704616066295 0.9918808401799004 0.9827086882453152 186103

Notes

  • For the production of this readme, i used the readme written by Pierre Guillou (available here) as a basis, reproducing some parts entirely.
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