LBenoit/EuroBert_mmBert

EU Detector

Binary classifier: is a parliamentary sentence about the European Union (EU=1) or not (EU=0)? Fine-tuned from jhu-clsp/mmBERT-base on hand-annotated parliamentary speeches from AUS, CZE, DEU, DNK, ESP, GBR, NLD, and SWE.

Labels

• 0 — Non-EU
• 1 — EU

Training

• Base model: jhu-clsp/mmBERT-base
• Max sequence length: 320
• Train/val/test split: leakage-safe (StratifiedGroupKFold on country × speech_ID)
• Loss: cross-entropy with balanced class weights

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tok = AutoTokenizer.from_pretrained("LBenoit/eu-detector-xlmr")
mdl = AutoModelForSequenceClassification.from_pretrained("LBenoit/eu-detector-xlmr")

text = "The European Commission proposed new climate targets."
enc  = tok(text, truncation=True, max_length=320, return_tensors="pt")
prob = torch.softmax(mdl(**enc).logits, dim=-1)[0, 1].item()
print(pred, prob)

Intended use

Research on parliamentary discourse about the EU. Outputs reflect the training corpus and annotation scheme; downstream prevalence estimates should ideally be calibrated against a base-rate-representative sample.