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+---
+layout: model
+title: Brazilian Portuguese NER for Laws (Bert, Base)
+author: John Snow Labs
+name: legner_lener_base
+date: 2024-05-15
+tags: [lener, laws, legal, licensed, ner, pt, tensorflow]
+task: Named Entity Recognition
+language: pt
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalBertForTokenClassification
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is a Deep Learning Portuguese Named Entity Recognition model for the legal domain, trained using Base Bert Embeddings, and is able to predict the following entities:
+
+- ORGANIZACAO (Organizations)
+- JURISPRUDENCIA (Jurisprudence)
+- PESSOA (Person)
+- TEMPO (Time)
+- LOCAL (Location)
+- LEGISLACAO (Laws)
+- O (Other)
+
+You can find different versions of this model in Models Hub:
+- With a Deep Learning architecture (non-transformer) and Base Embeddings;
+- With a Deep Learning architecture (non-transformer) and Large Embeddings;
+- With a Transformers Architecture and Base Embeddings;
+- With a Transformers Architecture and Large Embeddings;
+
+## Predicted Entities
+
+`PESSOA`, `ORGANIZACAO`, `LEGISLACAO`, `JURISPRUDENCIA`, `TEMPO`, `LOCAL`
+
+{:.btn-box}
+
+
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/legal/models/legner_lener_base_pt_1.0.0_3.0_1715772909273.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legner_lener_base_pt_1.0.0_3.0_1715772909273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = nlp.DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentenceDetector = nlp.SentenceDetectorDLModel.pretrained()\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = nlp.Tokenizer()\
+ .setInputCols("sentence")\
+ .setOutputCol("token")
+
+tokenClassifier = legal.BertForTokenClassification.load("legner_lener_base","pt", "legal/models")\
+ .setInputCols("token", "sentence")\
+ .setOutputCol("label")\
+ .setCaseSensitive(True)
+
+ner_converter = nlp.NerConverter()\
+ .setInputCols(["sentence","token","label"])\
+ .setOutputCol("ner_chunk")
+
+
+pipeline = nlp.Pipeline(
+ stages=[
+ documentAssembler,
+ sentenceDetector,
+ tokenizer,
+ tokenClassifier,
+ ner_converter
+ ]
+)
+
+example = spark.createDataFrame(pd.DataFrame({'text': ["""Mediante do exposto , com fundamento nos artigos 32 , i , e 33 , da lei 8.443/1992 , submetem-se os autos à consideração superior , com posterior encaminhamento ao ministério público junto ao tcu e ao gabinete do relator , propondo : a ) conhecer do recurso e , no mérito , negar-lhe provimento ; b ) comunicar ao recorrente , ao superior tribunal militar e ao tribunal regional federal da 2ª região , a fim de fornecer subsídios para os processos judiciais 2001.34.00.024796-9 e 2003.34.00.044227-3 ; e aos demais interessados a deliberação que vier a ser proferida por esta corte ” ."""]}))
+
+result = pipeline.fit(example).transform(example)
+```
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = nlp.DocumentAssembler()\
+ .setInputCol("text")\
+ .setOutputCol("document")
+
+sentenceDetector = nlp.SentenceDetectorDLModel.pretrained()\
+ .setInputCols(["document"])\
+ .setOutputCol("sentence")
+
+tokenizer = nlp.Tokenizer()\
+ .setInputCols("sentence")\
+ .setOutputCol("token")
+
+tokenClassifier = legal.BertForTokenClassification.load("legner_lener_large","pt", "legal/models")\
+ .setInputCols("token", "sentence")\
+ .setOutputCol("label")\
+ .setCaseSensitive(True)
+
+ner_converter = nlp.NerConverter()\
+ .setInputCols(["sentence","token","label"])\
+ .setOutputCol("ner_chunk")
+
+
+pipeline = nlp.Pipeline(
+ stages=[
+ documentAssembler,
+ sentenceDetector,
+ tokenizer,
+ tokenClassifier,
+ ner_converter
+ ]
+)
+
+example = spark.createDataFrame(pd.DataFrame({'text': ["""Mediante do exposto , com fundamento nos artigos 32 , i , e 33 , da lei 8.443/1992 , submetem-se os autos à consideração superior , com posterior encaminhamento ao ministério público junto ao tcu e ao gabinete do relator , propondo : a ) conhecer do recurso e , no mérito , negar-lhe provimento ; b ) comunicar ao recorrente , ao superior tribunal militar e ao tribunal regional federal da 2ª região , a fim de fornecer subsídios para os processos judiciais 2001.34.00.024796-9 e 2003.34.00.044227-3 ; e aos demais interessados a deliberação que vier a ser proferida por esta corte ” ."""]}))
+
+result = pipeline.fit(example).transform(example)
+```
+
+