Durrani95/eurobert-geopolitical-binary

EuroBERT Geopolitical Classifier (Binary)

Fine-tuned EuroBERT/EuroBERT-210m for binary classification of geopolitical tension in European news text.

• —Task: Sequence classification (binary)
• —Labels: non_geopolitical (0), geopolitical (1)
• —Intended use: Detects whether an article reflects geopolitical tension.
• —Languages: English, German, French, Spanish, Italian
• —Framework: 🤗 Transformers (PyTorch)

Quick start

Inference with transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Durrani95/eurobert-geopolitical-binary"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

texts = [
    "Energy Sanctions Deepen Divide Between Western Bloc and Major Oil Exporters.",
    "Military Exercises Near Disputed Waters Raise Fears of Regional Escalations.",

]

inputs = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=1)

for text, p in zip(texts, probs):
    label_id = int(p.argmax())
    label = model.config.id2label[label_id]
    confidence = float(p[label_id])
    print(f"{label:>16}  {confidence:6.2%}  | {text}")

Labels

{
  "0": "non_geopolitical",
  "1": "geopolitical"
}

You may apply a decision threshold (e.g., score >= 0.5) depending on your precision/recall trade-off.

Training & Evaluation

• —Base model: EuroBERT/EuroBERT-210m
• —Objective: Cross-entropy (binary)
• —Data: European news text labeled for geopolitical relevance
• —Hardware: A100 GPU
• —Epochs: 1
• —Optimizer: AdamW with linear scheduler
• —Metrics (validation set):

Training setup

Limitations & Risks

• —May be sensitive to domain shift (non-news, social media text)
• —Class imbalance can affect thresholding; calibrate on your validation data
• —Multilingual performance can vary across languages and registers

How to cite

If you use this model, please cite this repository and the EuroBERT base model.