Description
Pretrained Word Embeddings model, trained on legal contracts, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-uncased-contracts is a English model originally trained by nlpaueb.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","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)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","en")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("I love Spark NLP.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.embed.bert.contracts.uncased_base").predict("""I love Spark NLP.""")
Model Information
| Model Name: | bert_base_uncased_contracts |
| Compatibility: | Spark NLP 4.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | en |
| Size: | 410.0 MB |
| Case sensitive: | true |
References
https://huggingface.co/nlpaueb/bert-base-uncased-contracts