Description
The dataset consists of 12 documents taken from EUR-Lex, a multilingual corpus of court decisions and legal dispositions in the 24 official languages of the European Union.
This model extracts ADDRESS, AMOUNT, DATE, ORGANISATION, and PERSON entities from Spanish documents.
Predicted Entities
ADDRESS, AMOUNT, DATE, ORGANISATION, PERSON
How to use
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_embeddings_base_es_cased", "es")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")\
.setMaxSentenceLength(512)\
.setCaseSensitive(True)
ner_model = legal.NerModel.pretrained("legner_mapa", "es", "legal/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = nlp.NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = nlp.Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
embeddings,
ner_model,
ner_converter])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)
text = ["""Heiko Jonny Maniero , de nacionalidad italiana , nació y reside en Alemania."""]
result = model.transform(spark.createDataFrame([text]).toDF("text"))
Results
+-------------------+---------+
|chunk |ner_label|
+-------------------+---------+
|Heiko Jonny Maniero|PERSON |
|Alemania |ADDRESS |
+-------------------+---------+
Model Information
| Model Name: | legner_mapa |
| Compatibility: | Legal NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | es |
| Size: | 1.4 MB |
References
The dataset is available here.
Benchmarking
label precision recall f1-score support
ADDRESS 1.00 0.86 0.92 7
AMOUNT 1.00 1.00 1.00 1
DATE 1.00 0.92 0.96 24
ORGANISATION 0.83 0.71 0.77 7
PERSON 0.75 0.71 0.73 17
macro-avg 0.90 0.82 0.86 56
macro-avg 0.92 0.84 0.88 56
weighted-avg 0.90 0.82 0.86 56