Description
Pretrained BERT Embedding model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. legal-bert-base-uncased-finetuned-ledgarscotus7 is a English model originally trained by hatemestinbejaia.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased_finetuned_ledgarscotus7","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.bert.uncased_base_finetuned_legal.by_hatemestinbejaia").predict("""I love Spark NLP""")
Model Information
| Model Name: | bert_embeddings_legal_bert_base_uncased_finetuned_ledgarscotus7 |
| Compatibility: | Spark NLP 4.2.7+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence] |
| Output Labels: | [bert_sentence] |
| Language: | en |
| Size: | 409.9 MB |
| Case sensitive: | false |
| Max sentence length: | 128 |
References
- https://huggingface.co/hatemestinbejaia/legal-bert-base-uncased-finetuned-ledgarscotus7