Description
Pretrained Legal Longformer Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. legal-longformer-base is a English model originally trained by lexlms.
How to use
documentAssembler = DocumentAssembler() \
.setInputCols("text") \
.setOutputCols("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = LongformerEmbeddings.pretrained("longformer_base_english_legal","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()
.setInputCols(Array("text"))
.setOutputCols(Array("document"))
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val embeddings = LongformerEmbeddings.pretrained("longformer_base_english_legal","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: | longformer_base_english_legal |
| Compatibility: | Spark NLP 4.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 561.6 MB |
| Case sensitive: | true |
| Max sentence length: | 4096 |
References
https://huggingface.co/lexlms/legal-longformer-base