Description
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domains variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks. A light-weight model (33% the size of BERT-BASE) pre-trained from scratch on legal data with competitive perfomance is also available.
Predicted Entities
How to use
sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_base_uncased_legal", "en") \
.setInputCols("sentence") \
.setOutputCol("bert_sentence")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, sent_embeddings ])
val sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_base_uncased_legal", "en")
.setInputCols("sentence")
.setOutputCol("bert_sentence")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, sent_embeddings ))
import nlu
nlu.load("en.embed_sentence.bert.base_uncased_legal").predict("""Put your text here.""")
Model Information
| Model Name: | sent_bert_base_uncased_legal |
| Compatibility: | Spark NLP 3.2.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence] |
| Output Labels: | [bert_sentence] |
| Language: | en |
| Case sensitive: | true |
Data Source
The model is imported from: https://huggingface.co/nlpaueb/legal-bert-base-uncased