elenanereiss/bert-legal-ner
Legal NER with huggingface Transformers
This project helps legal professionals automatically identify and extract key entities from German legal texts. You can input raw legal documents and receive an output where specific terms like persons, locations, organizations, legal norms, court decisions, and contracts are tagged. It's designed for legal researchers, paralegals, or legal tech developers working with German legal documents who need to automate information extraction.
No commits in the last 6 months.
Use this if you need to precisely identify and categorize specific information within German legal texts, such as names of judges, types of laws, or court names.
Not ideal if your primary need is for legal entity recognition in languages other than German, or if you need to extract entities beyond the legal domain.
Stars
7
Forks
2
Language
Python
License
—
Category
Last pushed
Nov 04, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/elenanereiss/bert-legal-ner"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
chakki-works/seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
Hironsan/anago
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
jbesomi/texthero
Text preprocessing, representation and visualization from zero to hero.
hamelsmu/ktext
Utilities for preprocessing text for deep learning with Keras
asahi417/tner
Language model fine-tuning on NER with an easy interface and cross-domain evaluation. "T-NER: An...