README.md

---
language:
- pt
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoints
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
    metrics:
    - name: F1
      type: f1
      value: 0.9082022949426265
    - name: Precision
      type: precision
      value: 0.8975220495590088
    - name: Recall
      type: recall
      value: 0.9191397849462366
    - name: Accuracy
      type: accuracy
      value: 0.9808310603867311
    - name: Loss
      type: loss
      value: 0.1228889599442482
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9808615356484374
      verified: true
    - name: Precision
      type: precision
      value: 0.9853426225094238
      verified: true
    - name: Recall
      type: recall
      value: 0.9864880801954342
      verified: true
    - name: F1
      type: f1
      value: 0.9859150186480775
      verified: true
    - name: loss
      type: loss
      value: 0.11563518643379211
      verified: true
widget:
- text: "Ao Instituto M\xE9dico Legal da jurisdi\xE7\xE3o do acidente ou da resid\xEA\
    ncia cumpre fornecer, no prazo de 90 dias, laudo \xE0 v\xEDtima (art. 5, \xA7\
    \ 5, Lei n. 6.194/74  de 19 de dezembro de 1974), fun\xE7\xE3o t\xE9cnica que\
    \ pode ser suprida por prova pericial realizada por ordem do ju\xEDzo da causa,\
    \ ou por prova t\xE9cnica realizada no \xE2mbito administrativo que se mostre\
    \ coerente com os demais elementos de prova constante dos autos."
- text: "Acrescento que n\xE3o h\xE1 de se falar em viola\xE7\xE3o do artigo 114,\
    \ \xA7 3\xBA, da Constitui\xE7\xE3o Federal, posto que referido dispositivo revela-se\
    \ impertinente, tratando da possibilidade de ajuizamento de diss\xEDdio coletivo\
    \ pelo Minist\xE9rio P\xFAblico do Trabalho nos casos de greve em atividade essencial."
- text: "Todavia, entendo que extrair da aludida norma o sentido expresso na reda\xE7\
    \xE3o acima implica desconstruir o significado do texto constitucional, o que\
    \ \xE9 absolutamente vedado ao int\xE9rprete. Nesse sentido, cito Dimitri Dimoulis:\
    \ \u2018(...) ao int\xE9rprete n\xE3o \xE9 dado escolher significados que n\xE3\
    o estejam abarcados pela moldura da norma. Interpretar n\xE3o pode significar\
    \ violentar a norma.\u2019 (Positivismo Jur\xEDdico. S\xE3o Paulo: M\xE9todo,\
    \ 2006, p. 220).59. Dessa forma, deve-se tomar o sentido etimol\xF3gico como limite\
    \ da atividade interpretativa, a qual n\xE3o pode superado, a ponto de destruir\
    \ a pr\xF3pria norma a ser interpretada. Ou, como diz Konrad Hesse, \u2018o texto\
    \ da norma \xE9 o limite insuper\xE1vel da atividade interpretativa.\u2019 (Elementos\
    \ de Direito Constitucional da Rep\xFAblica Federal da Alemanha, Porto Alegre:\
    \ Sergio Antonio Fabris, 2003, p. 71)."
---

## (BERT large) NER model in the legal domain in Portuguese (LeNER-Br)

**ner-bert-large-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [pierreguillou/bert-large-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-large-cased-pt-lenerbr) on the dataset [LeNER_br](https://huggingface.co/datasets/lener_br) by using a NER objective.

Due to the small size of the finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset (*note: see the paragraph "Validation metrics by Named Entity" to get detailed metrics*):
  - **f1**: 0.9082022949426265
  - **precision**: 0.8975220495590088
  - **recall**: 0.9191397849462366
  - **accuracy**: 0.9808310603867311
  - **loss**: 0.1228889599442482
  
Check as well the [base version of this model](https://huggingface.co/pierreguillou/ner-bert-base-cased-pt-lenerbr) with a f1 of 0.893.

**Note**: the model [pierreguillou/bert-large-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-large-cased-pt-lenerbr) is a language model that was created through the finetuning of the model [BERTimbau large](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the dataset [LeNER-Br language modeling](https://huggingface.co/datasets/pierreguillou/lener_br_finetuning_language_model) by using a MASK objective. This first specialization of the language model before finetuning on the NER task allows to get a better NER model.

## 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.

Use as well the [NER App](https://huggingface.co/spaces/pierreguillou/ner-bert-pt-lenerbr) that allows comparing the 2 BERT models (base and large) fitted in the NER task with the legal LeNER-Br dataset.

## Using the model for inference in production
````
# install pytorch: check https://pytorch.org/
# !pip install transformers 
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

# parameters
model_name = "pierreguillou/ner-bert-large-cased-pt-lenerbr"
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.
````
!pip install transformers
import transformers
from transformers import pipeline

model_name = "pierreguillou/ner-bert-large-cased-pt-lenerbr"

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

ner(input_text)
````
## Training procedure

### Notebook

The notebook of finetuning ([HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb](https://github.com/piegu/language-models/blob/master/HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb)) is in github.

### Hyperparameters

# batch, learning rate...
- per_device_batch_size = 2
- gradient_accumulation_steps = 2
- learning_rate = 2e-5
- num_train_epochs = 10
- weight_decay = 0.01
- optimizer = AdamW
- betas = (0.9,0.999)
- epsilon = 1e-08
- lr_scheduler_type = linear
- seed = 42

# save model & load best model
- save_total_limit = 7
- logging_steps = 500
- 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

# get best model through a metric
- metric_for_best_model = 'eval_f1'
- greater_is_better = True

### Training results

````
Num examples = 7828
Num Epochs = 20
Instantaneous batch size per device = 2
Total train batch size (w. parallel, distributed & accumulation) = 4
Gradient Accumulation steps = 2
Total optimization steps = 39140

Step   Training Loss  Validation Loss  Precision  Recall    F1        Accuracy
500    0.250000       0.140582         0.760833   0.770323  0.765548  0.963125
1000   0.076200       0.117882         0.829082   0.817849  0.823428  0.966569
1500   0.082400       0.150047         0.679610   0.914624  0.779795  0.957213
2000   0.047500       0.133443         0.817678   0.857419  0.837077  0.969190
2500   0.034200       0.230139         0.895672   0.845591  0.869912  0.964070
3000   0.033800       0.108022         0.859225   0.887312  0.873043  0.973700
3500   0.030100       0.113467         0.855747   0.885376  0.870310  0.975879
4000   0.029900       0.118619         0.850207   0.884946  0.867229  0.974477
4500   0.022500       0.124327         0.841048   0.890968  0.865288  0.975041
5000   0.020200       0.129294         0.801538   0.918925  0.856227  0.968077
5500   0.019700       0.128344         0.814222   0.908602  0.858827  0.969250
6000   0.024600       0.182563         0.908087   0.866882  0.887006  0.968565
6500   0.012600       0.159217         0.829883   0.913763  0.869806  0.969357
7000   0.020600       0.183726         0.854557   0.893333  0.873515  0.966447
7500   0.014400       0.141395         0.777716   0.905161  0.836613  0.966828
8000   0.013400       0.139378         0.873042   0.899140  0.885899  0.975772
8500   0.014700       0.142521         0.864152   0.901505  0.882433  0.976366

9000   0.010900       0.122889         0.897522   0.919140  0.908202  0.980831

9500   0.013500       0.143407         0.816580   0.906667  0.859268  0.973395
10000  0.010400       0.144946         0.835608   0.908387  0.870479  0.974629
10500  0.007800       0.143086         0.847587   0.910108  0.877735  0.975985
11000  0.008200       0.156379         0.873778   0.884301  0.879008  0.976321
11500  0.008200       0.133356         0.901193   0.910108  0.905628  0.980328
12000  0.006900       0.133476         0.892202   0.920215  0.905992  0.980572
12500  0.006900       0.129991         0.890159   0.904516  0.897280  0.978683
````

### Validation metrics by Named Entity
````
{'JURISPRUDENCIA': {'f1': 0.8135593220338984,
  'number': 657,
  'precision': 0.865979381443299,
  'recall': 0.7671232876712328},
 'LEGISLACAO': {'f1': 0.8888888888888888,
  'number': 571,
  'precision': 0.8952042628774423,
  'recall': 0.882661996497373},
 'LOCAL': {'f1': 0.850467289719626,
  'number': 194,
  'precision': 0.7777777777777778,
  'recall': 0.9381443298969072},
 'ORGANIZACAO': {'f1': 0.8740635033892258,
  'number': 1340,
  'precision': 0.8373205741626795,
  'recall': 0.914179104477612},
 'PESSOA': {'f1': 0.9836677554829678,
  'number': 1072,
  'precision': 0.9841269841269841,
  'recall': 0.9832089552238806},
 'TEMPO': {'f1': 0.9669669669669669,
  'number': 816,
  'precision': 0.9481743227326266,
  'recall': 0.9865196078431373},
 'overall_accuracy': 0.9808310603867311,
 'overall_f1': 0.9082022949426265,
 'overall_precision': 0.8975220495590088,
 'overall_recall': 0.9191397849462366}
````