Input: "Die internationale Norm verbietet den Einsatz von Gewalt."
Output: IR sense (specialized usage)

Input: "Das entspricht nicht den technischen Normen in Deutschland."
Output: Colloquial sense (general usage)

Performance

F1 Score: 0.922 on held-out test set
Training / Test distribution: 70% IR sense, 30% colloquial (natural corpus distribution) | Term | F1 Score | |------|----------| | Entspannung | 0.967 | | Intervention | 0.949 | | Integration | 0.933 | | Kooperation | 0.926 | | Norm | 0.831 | | Regime | 0.828 |

Intended Use

German IR corpus analysis and text mining
Political science document classification
Automated terminology extraction in specialized texts
Production deployment on naturally distributed data

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "pdjohn/EuroBERT-610m-term-sense"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Classify a sentence
sentence = "Die internationale Norm verbietet den Einsatz von Gewalt."
inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=1).item()

# Interpretation
labels = {0: "Colloquial", 1: "IR Sense"}
print(f"Prediction: {labels[prediction]}")

Training Data

Corpus: German political science texts from IR literature
Size: 934 sentences (training) + 234 sentences (test)
Distribution: 70% IR sense, 30% colloquial (natural)
Terms: Norm, Kooperation, Regime, Integration, Intervention, Entspannung
Annotation: Manual expert annotation

Training Procedure

Base model: EuroBERT-610M
Fine-tuning method: LoRA (Low-Rank Adaptation)
- Rank (r): 8
- Alpha: 16
- Target modules: q_proj, k_proj, v_proj
- Dropout: 0.05
Epochs: 5
Batch size: 8
Learning rate: 1e-4
Optimizer: AdamW with cosine learning rate schedule

Limitations

Trained on imbalanced data (70/30 split)
Performance may drop on datasets with different distributions
see T-EBERT-Balanced for a variant with distribution robustness

Acknowledgements

This work was conducted as part of the Tracing International Institutions and Behavior (TIIB) project at TU Darmstadt.

Affiliation: Chair of Transnational Governance, Department of Political Science, Technische Universität Darmstadt

We thank the TIIB project team for their support and the manual annotation of the training corpus.