# 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]))

!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)

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

{'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}