Tudorx95/NER_Economic_Political_Spacy

spaCy NER Fine-tuned for Political & Economic Entities

Fine-tuned spaCy en_core_web_trf (RoBERTa-base backbone) on a custom politico-economic NER dataset. Trained to recognize 11 entity types.

Entity types

POLITICIAN, POLITICAL_PARTY, POLITICAL_ORG, FINANCIAL_ORG, ECONOMIC_INDICATOR, POLICY, LEGISLATION, MARKET_EVENT, CURRENCY, TRADE_AGREEMENT, GPE

Performance (Test Set)

spaCy native evaluation:

• —Precision: 0.8507
• —Recall: 0.7768
• —F1: 0.8121

nervaluate ent_type (comparabil cu GLiNER):

• —Precision: 0.8633
• —Recall: 0.7891
• —F1: 0.8245

Per label (ent_type):

Usage

import spacy

nlp = spacy.load("path/to/model")
# sau dupa descarcare de pe HuggingFace:
# from huggingface_hub import snapshot_download
# nlp = spacy.load(snapshot_download("Tudorx95/NER_Economic_Political_Spacy"))

doc = nlp("The Federal Reserve raised rates after President Biden signed the new bill.")
for ent in doc.ents:
    print(ent.text, "->", ent.label_)

Training Details

• —Framework: spaCy 3.8.14
• —Backbone: encoreweb_trf (RoBERTa-base)
• —Strategy: Frozen transformer + NER head fine-tuning
• —Train examples: 5747
• —Dev examples: 1228
• —Test examples: 2124
• —Best dev F1: 0.8374