Description
Pretrained BERT Embedding model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. legalbert is a English model originally trained by zlucia.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_embeddings_legalbert","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.embed.legalbert.legal.by_zlucia").predict("""I love Spark NLP""")
Model Information
| Model Name: | bert_embeddings_legalbert |
| Compatibility: | Spark NLP 4.2.7+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence] |
| Output Labels: | [bert_sentence] |
| Language: | en |
| Size: | 409.6 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/zlucia/legalbert
- https://case.law/
- https://arxiv.org/abs/2104.08671
- https://github.com/reglab/casehold