--- 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 widget: - text: "Ao Instituto Médico Legal da jurisdição do acidente ou da residência cumpre fornecer, no prazo de 90 dias, laudo à vítima (art. 5, § 5, Lei n. 6.194/74 de 19 de dezembro de 1974), função técnica que pode ser suprida por prova pericial realizada por ordem do juízo da causa, ou por prova técnica realizada no âmbito administrativo que se mostre coerente com os demais elementos de prova constante dos autos." - 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." - text: "Todavia, entendo que extrair da aludida norma o sentido expresso na redação acima implica desconstruir o significado do texto constitucional, o que é absolutamente vedado ao intérprete. Nesse sentido, cito Dimitri Dimoulis: ‘(...) ao intérprete não é dado escolher significados que não estejam abarcados pela moldura da norma. Interpretar não pode significar violentar a norma.’ (Positivismo Jurídico. São Paulo: Método, 2006, p. 220).59. Dessa forma, deve-se tomar o sentido etimológico como limite da atividade interpretativa, a qual não pode superado, a ponto de destruir a própria norma a ser interpretada. Ou, como diz Konrad Hesse, ‘o texto da norma é o limite insuperável da atividade interpretativa.’ (Elementos de Direito Constitucional da República 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} ````