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dominguesm
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legal-bert-ner-base-cased-ptbr

Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
Portuguese
bert
generated_from_trainer
Eval Results
Inference Endpoints
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  1. README.md +83 -0
  2. config.json +62 -0
  3. pytorch_model.bin +3 -0
  4. runs/e3_lr2e-05/1665668964.0356135/events.out.tfevents.1665668964.9dd23284ee7b.94.1 +3 -0
  5. runs/e3_lr2e-05/1665669018.9886503/events.out.tfevents.1665669018.9dd23284ee7b.94.3 +3 -0
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  14. runs/e3_lr2e-05/events.out.tfevents.1665705195.9dd23284ee7b.1181.2 +3 -0
  15. special_tokens_map.json +7 -0
  16. tokenizer.json +0 -0
  17. tokenizer_config.json +15 -0
  18. training_args.bin +3 -0
  19. vocab.txt +0 -0
README.md CHANGED
@@ -1,3 +1,86 @@
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  ---
 
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ thumbnail: "https://i.imgur.com/tpI1iT5.jpg"
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  license: apache-2.0
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+ language:
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+ - pt
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: checkpoints
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.9525622169191057
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+ - name: Precision
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+ type: precision
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+ value: 0.9438680702115613
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+ - name: Recall
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+ type: recall
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+ value: 0.961418019517758
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9894253721279602
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+ - name: Loss
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+ type: loss
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+ value: 0.030161771923303604
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+ widget:
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+ - 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."
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+ - 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."
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+ - text: "Dispõe sobre o estágio de estudantes; altera a redação do art. 428 da Consolidação das Leis do Trabalho – 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 março de 1994, o parágrafo único do art. 82 da Lei no 9.394, de 20 de dezembro de 1996, e o art. 6o da Medida Provisória no 2.164-41, de 24 de agosto de 2001; e dá outras providências."
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  ---
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+
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+ ## (BERT base) NER model in the legal domain in Portuguese
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+
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+ **README under construction**
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+
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+ **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](https://huggingface.co/dominguesm/legal-bert-base-cased-ptbr) by using a NER objective.
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+
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+ The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese were used (more information below).
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+
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+
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+ ## Training procedure
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+
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+ ### Training results
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+
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+ ```
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+ Num examples = 971932
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+ Num Epochs = 3
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+ Instantaneous batch size per device = 64
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+ Total train batch size (w. parallel, distributed & accumulation) = 128
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+ Gradient Accumulation steps = 2
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+ Total optimization steps = 22779
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+ ```
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+
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+ | Step | Training Loss | Validation Loss | Precision | Recall | F1 Accuracy |
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+ | ---- | ------------- | --------------- | --------- | ------ | ----------- |
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+ |1000 |0.113900 | 0.057008 | 0.898600| 0.938444| 0.918090| 0.980961|
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+ |2000 |0.052800 | 0.048254 | 0.917243 | 0.941188 | 0.929062 | 0.983854|
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+ |3000 |0.046200 | 0.043833 | 0.919706 | 0.948411 | 0.933838 | 0.984931|
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+ |4000 |0.043500 | 0.039796 | 0.928439 | 0.947058 | 0.937656 | 0.985891|
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+ |5000 |0.041400 | 0.039421 | 0.926103 | 0.952857 | 0.939290 | 0.986130|
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+ |6000 |0.039700 | 0.038599 | 0.922376 | 0.956257 | 0.939011 | 0.986093|
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+ |7000 |0.037800 | 0.036463 | 0.935125 | 0.950937 | 0.942964 | 0.987030|
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+ |8000 |0.035900 | 0.035706 | 0.934638 | 0.954147 | 0.944292 | 0.987433|
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+ |9000 |0.033800 | 0.034518 | 0.940354 | 0.951991 | 0.946136 | 0.987866|
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+ |10000 |0.033600 | 0.033454 | 0.938170 | 0.956097 | 0.947049 | 0.988066|
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+ |11000 |0.032700 | 0.032899 | 0.934130 | 0.959491 | 0.946641 | 0.988092|
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+ |12000 |0.032200 | 0.032477 | 0.937400 | 0.959150 | 0.948151 | 0.988305|
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+ |13000 |0.031200 | 0.033207 | 0.937058 | 0.960506 | 0.948637 | 0.988340|
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+ |14000 |0.031400 | 0.031711 | 0.938765 | 0.959711 | 0.949123 | 0.988635|
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+ |15000 |0.030600 | 0.031519 | 0.940488 | 0.959413 | 0.949856 | 0.988709|
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+ |16000 |0.028500 | 0.031618 | 0.943643 | 0.957693 | 0.950616 | |0.988891|
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+ |17000 |0.028000 | 0.031106 | 0.941109 | 0.960687 | 0.950797 | |0.989016|
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+ |18000 |0.027800 | 0.030712 | 0.942821 | 0.960528 | 0.951592 | |0.989198|
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+ |19000 |0.027500 | 0.030523 | 0.942950 | 0.960947 | 0.951864 | |0.989348|
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+ |20000 |0.027400 | 0.030577 | 0.942462 | 0.961754 | 0.952010 | |0.989295|
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+ |21000 |0.027000 | 0.030025 | 0.944483 | 0.960497 | 0.952422 | |0.989445|
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+ |22000 |0.026800 | 0.030162 | 0.943868 | 0.961418 | 0.952562 | |0.989425|
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+ "pooler_type": "first_token_transform",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.23.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 29794
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