Description
Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-uncased-issues-128 is a English model originally trained by antoinev17
.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings") \
.setCaseSensitive(True)
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
.setCaseSensitive(True)
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("I love Spark NLP").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | bert_embeddings_base_uncased_issues_128 |
| Compatibility: | Spark NLP 5.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | en |
| Size: | 407.1 MB |
| Case sensitive: | true |