Description
Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. deberta-v3-base-lm is a English model originally trained by iewaij.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_v3_base_lm","en") \
.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 = DeBertaEmbeddings.pretrained("deberta_embeddings_v3_base_lm","en")
.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: | deberta_embeddings_v3_base_lm |
| Compatibility: | Spark NLP 4.3.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 691.5 MB |
| Case sensitive: | false |
References
https://huggingface.co/iewaij/deberta-v3-base-lm