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thechancerylaneproject/legal-sustainability-ner

sourceHugging Facemitupdated 5mo agoView on Hugging Face
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Model Card

Legal Sustainability NER

A bespoke spaCy named entity recognition model fine-tuned on legal text from The Chancery Lane Project (TCLP) — a collection of climate-focused contract clauses and glossary terms.

Fine-tuned on top of nlpaueb/legal-bert-base-uncased.

Used as part of the TCLP Knowledge Graph pipeline.

Entity labels

LabelDescription
Clause Name (TCLP-specific)A named TCLP clause (e.g. "Marni's Clause")
Glossary Term (TCLP-specific)A named TCLP glossary term (e.g. "Net Zero Target")
ConceptA sustainability or legal concept (e.g. "carbon footprint", "force majeure")
Defined TermA term defined within a contract (e.g. "Supplier", "Business Days")
Party to ContractA contracting party (e.g. "Landlord", "Contractor")
OrgAn organisation (e.g. "Science Based Targets Initiative")
StandardA standard or framework (e.g. "ISO 27001", "GHG Protocol")
LawA piece of legislation (e.g. "Data Protection Act 2018")
PersonA named individual
ObjectA physical or tangible object
SectorAn industry or sector
OtherEntities that don't fit another category
UNKNOWNUnclassifiable entities

Usage

python
import spacy
from huggingface_hub import snapshot_download

# Download model
model_path = snapshot_download("georgiaray/legal-sustainability-ner")

# Load
nlp = spacy.load(model_path + "/model-last")

# Run
doc = nlp("The Supplier must comply with the GHG Protocol and report Scope 1, 2 and 3 emissions.")
for ent in doc.ents:
    print(ent.text, "->", ent.label_)

Files

FileDescription
model-last/Latest model checkpoint
entities.dbSQLite database of ~2,200 hand-curated TCLP entity keywords, used for keyword matching alongside the NER model

Performance

~73% accuracy on entity type classification, evaluated against a hand-labelled dataset of TCLP entities. This is a subjective multi-label task where reasonable disagreement exists between labels (e.g. a term may be both a Defined Term and a Party to Contract).