README.md

metadata

language: multilingual
license: mit
tags:
  - text-classification
  - political-science
  - euroscepticism
  - parliamentary-speech
base_model: jhu-clsp/mmBERT-base

EU_Ukraine

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, including Ukrainian Rada data alongside other European parliaments.

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_Ukraine")
mdl = AutoModelForSequenceClassification.from_pretrained("LBenoit/EU_Ukraine")

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