Description
Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. lsg16k-Italian-Legal-BERT is a Italian model originally trained by dlicari.
How to use
documentAssembler = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = nlp.Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_lsg16k_Italian_Legal","it") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = nlp.Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained("bert_embeddings_lsg16k_Italian_Legal","it")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("Adoro Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | bert_embeddings_lsg16k_Italian_Legal |
| Compatibility: | Spark NLP 4.3.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | it |
| Size: | 457.4 MB |
| Case sensitive: | true |
References
https://huggingface.co/dlicari/lsg16k-Italian-Legal-BERT