2022-11-30-legner_ronec_ro

docs/_posts/bunyamin-polat/2022-11-30-legner_ronec_ro.md

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+---
+layout: model
+title: Legal Romanian NER (RONEC dataset)
+author: John Snow Labs
+name: legner_ronec
+date: 2022-11-30
+tags: [ro, ner, legal, ronec, licensed]
+task: Named Entity Recognition
+language: ro
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `legner_ronec` is a Named Entity Recognition model trained on RONEC (ROmanian Named Entity Corpus). Unlike the original dataset, it has been trained with the following classes:
+
+- PERSON - proper nouns or pronouns if they refer to a person
+- LOC - location or geo political entity
+- ORG - organization
+- LANGUAGE - language
+- NAT_REL_POL - national, religious or political organizations
+- DATETIME - a time and date in any format, including references to time (e.g. 'yesterday')
+- MONEY - a monetary value, numeric or otherwise
+- NUMERIC - a simple numeric value, represented as digits or words
+- ORDINAL - an ordinal value like 'first', 'third', etc.
+- WORK_OF_ART - a work of art like a named TV show, painting, etc.
+- EVENT - a named recognizable or periodic major event
+
+## Predicted Entities
+
+`DATETIME`, `EVENT`, `LANGUAGE`, `LOC`, `MONEY`, `NAT_REL_POL`, `NUMERIC`, `ORDINAL`, `ORG`, `PERSON`, `WORK_OF_ART`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/legal/models/legner_ronec_ro_1.0.0_3.0_1669842840646.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+document_assembler = nlp.DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+sentence_detector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
+    .setInputCols(["document"])\
+    .setOutputCol("sentence")
+
+tokenizer = nlp.Tokenizer()\
+    .setInputCols(["sentence"])\
+    .setOutputCol("token")
+
+embeddings = nlp.BertEmbeddings.pretrained("bert_base_cased", "ro")\
+    .setInputCols("sentence", "token")\
+    .setOutputCol("embeddings")\
+    .setMaxSentenceLength(512)\
+    .setCaseSensitive(True)
+
+ner_model = legal.NerModel.pretrained("legner_ronec", "ro", "legal/models")\
+    .setInputCols(["sentence", "token", "embeddings"])\
+    .setOutputCol("ner")
+
+ner_converter = nlp.NerConverter()\
+    .setInputCols(["sentence", "token", "ner"])\
+    .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    embeddings,
+    ner_model,
+    ner_converter   
+    ])
+
+model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
+
+data = spark.createDataFrame([["""Guvernul de stânga italian, condus de premierul Romano Prodi, a devenit după numirea a încă trei secretari de stat, cel mai numeros Executiv din istoria Republicii italiene, având 102 membri."""]]).toDF("text")
+
+result = model.transform(data)
+```
+
+</div>
+
+## Results
+
+```bash
++----------------------+-----------+
+|ner_chunk             |label      |
++----------------------+-----------+
+|Guvernul              |ORG        |
+|italian               |NAT_REL_POL|
+|premierul Romano Prodi|PERSON     |
+|trei                  |NUMERIC    |
+|secretari             |PERSON     |
+|Republicii italiene   |LOC        |
+|102                   |NUMERIC    |
+|membri                |PERSON     |
++----------------------+-----------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legner_ronec|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|ro|
+|Size:|16.2 MB|
+
+## References
+
+Dataset is available [here](https://github.com/dumitrescustefan/ronec).
+
+## Benchmarking
+
+```bash
+label         precision  recall  f1-score  support
+
+DATETIME      0.90       0.90    0.90      1070
+EVENT         0.53       0.68    0.59      116
+LANGUAGE      0.98       0.95    0.97      44
+LOC           0.91       0.90    0.91      1699
+MONEY         0.97       0.97    0.97      130
+NAT_REL_POL   0.92       0.94    0.93      510
+NUMERIC       0.95       0.95    0.95      970
+ORDINAL       0.88       0.93    0.90      183
+ORG           0.81       0.83    0.82      779
+PERSON        0.89       0.91    0.90      2635
+WORK_OF_ART   0.73       0.57    0.64      140
+
+micro-avg     0.89       0.90    0.89      8276
+macro-avg     0.86       0.87    0.86      8276
+weighted-avg  0.89       0.90    0.89      8276
+```