autoevaluator
HF staff
Add evaluation results on the lener_br config and validation split of lener_br
169a6e9
| 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: 0.8926146010186757 | |
| - name: Precision | |
| type: precision | |
| value: 0.8810222036028488 | |
| - name: Recall | |
| type: recall | |
| value: 0.9045161290322581 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9759397808828684 | |
| - name: Loss | |
| type: loss | |
| value: 0.18803243339061737 | |
| - 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: 0.9725875020951742 | |
| verified: true | |
| - name: Precision | |
| type: precision | |
| value: 0.9796906680965867 | |
| verified: true | |
| - name: Recall | |
| type: recall | |
| value: 0.9850054755285991 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 0.9823408831238658 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 0.19303591549396515 | |
| 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: "Disp\xF5e sobre o est\xE1gio de estudantes; altera a reda\xE7\xE3o do art.\ | |
| \ 428 da Consolida\xE7\xE3o das Leis do Trabalho \u2013 CLT, aprovada pelo Decreto-Lei\ | |
| \ no 5.452, de 1o de maio de 1943, e a Lei no 9.394, de 20 de dezembro de 1996;\ | |
| \ revoga as Leis nos 6.494, de 7 de dezembro de 1977, e 8.859, de 23 de mar\xE7\ | |
| o de 1994, o par\xE1grafo \xFAnico do art. 82 da Lei no 9.394, de 20 de dezembro\ | |
| \ de 1996, e o art. 6o da Medida Provis\xF3ria no 2.164-41, de 24 de agosto de\ | |
| \ 2001; e d\xE1 outras provid\xEAncias." | |
| ## (BERT base) NER model in the legal domain in Portuguese (LeNER-Br) | |
| **ner-bert-base-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-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-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 BERTimbau base and 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**: 0.8926146010186757 | |
| - **precision**: 0.8810222036028488 | |
| - **recall**: 0.9045161290322581 | |
| - **accuracy**: 0.9759397808828684 | |
| - **loss**: 0.18803243339061737 | |
| Check as well the [large version of this model](https://huggingface.co/pierreguillou/ner-bert-large-cased-pt-lenerbr) with a f1 of 0.908. | |
| **Note**: the model [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr) is a language model that was created through the finetuning of the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-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 improved a bit the model quality. To prove it, here are the results of the NER model finetuned from the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) (a non-specialized language model): | |
| - **f1**: 0.8716487228203504 | |
| - **precision**: 0.8559286898839138 | |
| - **recall**: 0.8879569892473118 | |
| - **accuracy**: 0.9755893153732458 | |
| - **loss**: 0.1133928969502449 | |
| ## 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-base-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-base-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 = 7 | |
| #### save model & load best model | |
| - save_total_limit = 2 | |
| - logging_steps = 300 | |
| - 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 = 10 | |
| Instantaneous batch size per device = 2 | |
| Total train batch size (w. parallel, distributed & accumulation) = 4 | |
| Gradient Accumulation steps = 2 | |
| Total optimization steps = 19570 | |
| Step Training Loss Validation Loss Precision Recall F1 Accuracy | |
| 300 0.127600 0.178613 0.722909 0.741720 0.732194 0.948802 | |
| 600 0.088200 0.136965 0.733636 0.867742 0.795074 0.963079 | |
| 900 0.078000 0.128858 0.791912 0.838065 0.814335 0.965243 | |
| 1200 0.077800 0.126345 0.815400 0.865376 0.839645 0.967849 | |
| 1500 0.074100 0.148207 0.779274 0.895914 0.833533 0.960184 | |
| 1800 0.059500 0.116634 0.830829 0.868172 0.849090 0.969342 | |
| 2100 0.044500 0.208459 0.887150 0.816559 0.850392 0.960535 | |
| 2400 0.029400 0.136352 0.867821 0.851398 0.859531 0.970271 | |
| 2700 0.025000 0.165837 0.814881 0.878495 0.845493 0.961235 | |
| 3000 0.038400 0.120629 0.811719 0.893763 0.850768 0.971506 | |
| 3300 0.026200 0.175094 0.823435 0.882581 0.851983 0.962957 | |
| 3600 0.025600 0.178438 0.881095 0.886022 0.883551 0.963689 | |
| 3900 0.041000 0.134648 0.789035 0.916129 0.847846 0.967681 | |
| 4200 0.026700 0.130178 0.821275 0.903226 0.860303 0.972313 | |
| 4500 0.018500 0.139294 0.844016 0.875054 0.859255 0.971140 | |
| 4800 0.020800 0.197811 0.892504 0.873118 0.882705 0.965883 | |
| 5100 0.019300 0.161239 0.848746 0.888172 0.868012 0.967849 | |
| 5400 0.024000 0.139131 0.837507 0.913333 0.873778 0.970591 | |
| 5700 0.018400 0.157223 0.899754 0.864731 0.881895 0.970210 | |
| 6000 0.023500 0.137022 0.883018 0.873333 0.878149 0.973243 | |
| 6300 0.009300 0.181448 0.840490 0.900860 0.869628 0.968290 | |
| 6600 0.019200 0.173125 0.821316 0.896559 0.857290 0.966736 | |
| 6900 0.016100 0.143160 0.789938 0.904946 0.843540 0.968245 | |
| 7200 0.017000 0.145755 0.823274 0.897634 0.858848 0.969037 | |
| 7500 0.012100 0.159342 0.825694 0.883226 0.853491 0.967468 | |
| 7800 0.013800 0.194886 0.861237 0.859570 0.860403 0.964771 | |
| 8100 0.008000 0.140271 0.829914 0.896129 0.861752 0.971567 | |
| 8400 0.010300 0.143318 0.826844 0.908817 0.865895 0.973578 | |
| 8700 0.015000 0.143392 0.847336 0.889247 0.867786 0.973365 | |
| 9000 0.006000 0.143512 0.847795 0.905591 0.875741 0.972892 | |
| 9300 0.011800 0.138747 0.827133 0.894194 0.859357 0.971673 | |
| 9600 0.008500 0.159490 0.837030 0.909032 0.871546 0.970028 | |
| 9900 0.010700 0.159249 0.846692 0.910968 0.877655 0.970546 | |
| 10200 0.008100 0.170069 0.848288 0.900645 0.873683 0.969113 | |
| 10500 0.004800 0.183795 0.860317 0.899355 0.879403 0.969570 | |
| 10800 0.010700 0.157024 0.837838 0.906667 0.870894 0.971094 | |
| 11100 0.003800 0.164286 0.845312 0.880215 0.862410 0.970744 | |
| 11400 0.009700 0.204025 0.884294 0.887527 0.885907 0.968854 | |
| 11700 0.008900 0.162819 0.829415 0.887742 0.857588 0.970530 | |
| 12000 0.006400 0.164296 0.852666 0.901075 0.876202 0.971414 | |
| 12300 0.007100 0.143367 0.852959 0.895699 0.873807 0.973669 | |
| 12600 0.015800 0.153383 0.859224 0.900430 0.879345 0.972679 | |
| 12900 0.006600 0.173447 0.869954 0.899140 0.884306 0.970927 | |
| 13200 0.006800 0.163234 0.856849 0.897204 0.876563 0.971795 | |
| 13500 0.003200 0.167164 0.850867 0.907957 0.878485 0.971231 | |
| 13800 0.003600 0.148950 0.867801 0.910538 0.888656 0.976961 | |
| 14100 0.003500 0.155691 0.847621 0.907957 0.876752 0.974127 | |
| 14400 0.003300 0.157672 0.846553 0.911183 0.877680 0.974584 | |
| 14700 0.002500 0.169965 0.847804 0.917634 0.881338 0.973045 | |
| 15000 0.003400 0.177099 0.842199 0.912473 0.875929 0.971155 | |
| 15300 0.006000 0.164151 0.848928 0.911183 0.878954 0.973258 | |
| 15600 0.002400 0.174305 0.847437 0.906667 0.876052 0.971765 | |
| 15900 0.004100 0.174561 0.852929 0.907957 0.879583 0.972907 | |
| 16200 0.002600 0.172626 0.843263 0.907097 0.874016 0.972100 | |
| 16500 0.002100 0.185302 0.841108 0.907312 0.872957 0.970485 | |
| 16800 0.002900 0.175638 0.840557 0.909247 0.873554 0.971704 | |
| 17100 0.001600 0.178750 0.857056 0.906452 0.881062 0.971765 | |
| 17400 0.003900 0.188910 0.853619 0.907957 0.879950 0.970835 | |
| 17700 0.002700 0.180822 0.864699 0.907097 0.885390 0.972283 | |
| 18000 0.001300 0.179974 0.868150 0.906237 0.886785 0.973060 | |
| 18300 0.000800 0.188032 0.881022 0.904516 0.892615 0.972572 | |
| 18600 0.002700 0.183266 0.868601 0.901290 0.884644 0.972298 | |
| 18900 0.001600 0.180301 0.862041 0.903011 0.882050 0.972344 | |
| 19200 0.002300 0.183432 0.855370 0.904301 0.879155 0.971109 | |
| 19500 0.001800 0.183381 0.854501 0.904301 0.878696 0.971186 | |
| ```` | |
| ### Validation metrics by Named Entity | |
| ```` | |
| Num examples = 1177 | |
| {'JURISPRUDENCIA': {'f1': 0.7016574585635359, | |
| 'number': 657, | |
| 'precision': 0.6422250316055625, | |
| 'recall': 0.7732115677321156}, | |
| 'LEGISLACAO': {'f1': 0.8839681133746677, | |
| 'number': 571, | |
| 'precision': 0.8942652329749103, | |
| 'recall': 0.8739054290718039}, | |
| 'LOCAL': {'f1': 0.8253968253968254, | |
| 'number': 194, | |
| 'precision': 0.7368421052631579, | |
| 'recall': 0.9381443298969072}, | |
| 'ORGANIZACAO': {'f1': 0.8934049079754601, | |
| 'number': 1340, | |
| 'precision': 0.918769716088328, | |
| 'recall': 0.8694029850746269}, | |
| 'PESSOA': {'f1': 0.982653539615565, | |
| 'number': 1072, | |
| 'precision': 0.9877474081055608, | |
| 'recall': 0.9776119402985075}, | |
| 'TEMPO': {'f1': 0.9657657657657657, | |
| 'number': 816, | |
| 'precision': 0.9469964664310954, | |
| 'recall': 0.9852941176470589}, | |
| 'overall_accuracy': 0.9725722644643211, | |
| 'overall_f1': 0.8926146010186757, | |
| 'overall_precision': 0.8810222036028488, | |
| 'overall_recall': 0.9045161290322581} | |
| ```` |