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rwillh11
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mdberta-token-bilingual-noContext_Enhanced

Token Classification
Transformers
Safetensors
deberta-v2
political-science
multilingual
deberta
group-appeals
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Model Card for mDeBERTa Token Classification

A multilingual token classification model fine-tuned for detecting and extracting group mentions in political text.

Model Details

Model Description

This model is a fine-tuned mDeBERTa-v3-base that performs token-level classification to identify group mentions in political text. The model processes text units without additional context and predicts BIO (Begin-Inside-Outside) tags for token-level group mention detection.

  • Developed by: Will Horne, Alona O. Dolinsky and Lena Maria Huber
  • Model type: Token Classification
  • Language(s) (NLP): English, German (multilingual)
  • Finetuned from model: microsoft/mdeberta-v3-base

Model Sources

Uses

Direct Use

The model is designed for researchers analyzing political discourse to identify specific group mentions in political text, trained and validated using party manifestos. It takes natural sentences as input and identifies spans of text that refer to specific target groups.

Downstream Use

This model can be integrated into larger political text analysis pipelines for:

  • Political manifestos analysis
  • Group appeals detection in political communication
  • Comparative political research across countries and languages
  • Preprocessing step for stance detection models

Out-of-Scope Use

This model should not be used for:

  • General named entity recognition (not group-specific)
  • Real-time social media monitoring without human oversight
  • Making decisions about individuals or groups
  • Content moderation without additional validation

Bias, Risks, and Limitations

Technical Limitations

  • Trained specifically on political manifesto text; performance may vary on other text types
  • Focus sentences without context may lack nuance present in full paragraphs
  • Limited to binary classification (group mention vs. non-group mention)

Bias Considerations

  • Training data consists of political manifestos from specific countries and time periods
  • May reflect biases present in political discourse of training data
  • Group detection may vary across different political contexts and group types

How to Get Started with the Model 

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

# Load model and tokenizer
model_name = "rwillh11/mdberta-token-bilingual-noContext_Enhanced"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()

# Example usage
text = "We will increase funding for schools to better support students and teachers."

def extract_group_mentions(text):
    # Tokenize with offset mapping
    encoded = tokenizer(text, return_offsets_mapping=True, truncation=True, 
                       padding='max_length', max_length=512, return_tensors="pt")
    
    # Move to device
    input_ids = encoded['input_ids'].to(device)
    attention_mask = encoded['attention_mask'].to(device)
    offset_mapping = encoded['offset_mapping'][0]
    
    # Get predictions
    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
        predictions = torch.argmax(outputs.logits, dim=-1)[0]
    
    # Convert to character spans
    group_mentions = []
    current_start = None
    
    for i, (pred, (start_char, end_char)) in enumerate(zip(predictions, offset_mapping)):
        if start_char == end_char:  # Skip special tokens
            continue
            
        if pred == 1:  # Group mention token
            if current_start is None:
                current_start = start_char.item()
            current_end = end_char.item()
        else:  # Non-group token
            if current_start is not None:
                mention_text = text[current_start:current_end]
                group_mentions.append((current_start, current_end, mention_text))
                current_start = None
    
    # Handle mention at end of sequence
    if current_start is not None:
        mention_text = text[current_start:current_end]
        group_mentions.append((current_start, current_end, mention_text))
    
    return group_mentions

# Extract group mentions
mentions = extract_group_mentions(text)
print(f"Group mentions found: {mentions}")
# Output: [(63, 71, 'students'), (76, 84, 'teachers')]

Training Details

Training Data

The model was trained on political manifesto data containing:

  • Languages: English and German (bilingual)
  • Text Type: Political manifesto sentences at natural sentence level
  • Labels: BIO token classification (B-GROUP, I-GROUP, O)
  • Groups: Various political target groups (citizens, specific demographics, etc.)
  • Total Dataset: 7,589 examples
    • German: 4,753 examples (62.6%)
    • English: 2,836 examples (37.4%)
  • Training Split: 80/20 deterministic split (seed=42)
    • Training Set: ~6,071 examples (German: ~3,802, English: ~2,269)
    • Validation Set: ~1,518 examples (German: ~951, English: ~567)

Training Hyperparameters

  • Training regime: Mixed precision training
  • Optimizer: AdamW with weight decay
  • Learning rate: 2.92e-05 (optimized via Optuna)
  • Weight decay: 0.282 (optimized via Optuna)
  • Warmup ratio: 0.098 (optimized via Optuna)
  • Epochs: 10 per trial
  • Batch size: 16 (train and eval)
  • Trials: 20 total hyperparameter optimization trials
  • Metric for selection: F1 Macro
  • Seed: 42 (deterministic training)

Training Infrastructure

  • Hardware: CUDA-enabled GPU
  • Framework: Transformers, PyTorch
  • Hyperparameter optimization: Optuna
  • Deterministic training: All random seeds fixed

Evaluation

Testing Data, Factors & Metrics

Testing Data

  • 20% holdout from original dataset (~1,518 examples)
  • Multilingual political manifesto sentences (62.6% German, 37.4% English)
  • Evaluated across entity classes and both languages

Factors

The model was evaluated across:

  • Languages: English and German text
  • Additional validation on held out sets: English, German, Dutch, Danish, Spanish, French, Italian, Norwegian, Swedish
  • Group types: Various political target groups

Results

Best Model Performance (Trial 10 Epoch 7):

  • Accuracy: 0.976
  • Balanced Accuracy: 0.961
  • Precision: 0.953
  • Recall: 0.961
  • F1 Macro: 0.957

Additional validation on held-out sets return the following metrics:

English

  • Precision: 0.93
  • Recall: 0.912
  • F1 Macro: 0.921

German (using spaCy to accommodate relevant morphological differences)

  • Precision: 0.889
  • Recall: 0.868
  • F1 Macro: 0.878

Danish (using texts translated from English)

  • Precision: 0.836
  • Recall: 0.818
  • F1 Macro: 0.827

Spanish (using texts translated from English)

  • Precision: 0.907
  • Recall: 0.889
  • F1 Macro: 0.898

Dutch (using texts translated from English)

  • Precision: 0.875
  • Recall: 0.891
  • F1 Macro: 0.883

French (using texts translated from English)

  • Precision: 0.877
  • Recall: 0.875
  • F1 Macro: 0.876

Italian (using texts translated from English)

  • Precision: 0.916
  • Recall: 0.899
  • F1 Macro: 0.908

Swedish (using texts translated from English)

  • Precision: 0.867
  • Recall: 0.853
  • F1 Macro: 0.86

The model demonstrates excellent performance in identifying group mentions at the token level with strong precision and recall across both languages.

Environmental Impact

Training involved hyperparameter optimization with 20 trials, each training for 10 epochs.

  • Hardware Type: CUDA-enabled GPU
  • Hours used: Estimated 15-20 hours (including hyperparameter search)
  • Cloud Provider: Google Colab

Technical Specifications

Model Architecture and Objective

  • Base Architecture: mDeBERTa-v3-base (278M parameters)
  • Task: Token Classification for Named Entity Recognition
  • Input: Political sentence tokens
  • Objective: Cross-entropy loss with F1 Macro optimization

Software

  • Transformers library
  • PyTorch framework
  • Optuna for hyperparameter optimization
  • scikit-learn for metrics

Citation

If you use this model in your research, please cite:

BibTeX:

@misc{mdberta_token_nocontext_enhanced,
  title={mDeBERTa Token Classification Model for Political Group Appeals Detection},
  author={Horne, Will and Alona Dolinsky and Lena Maria Huber},
  year={2025},
  url={https://huggingface.co/rwillh11/mdberta-token-bilingual-noContext_Enhanced}
}

Model Card Authors

Will Horne is an assistant professor of Political Science at Clemson University. Alona Dolinsky is a Research Associate at the Department of Communication Science at Vrije Universiteit Amsterdam. Lena Maria Huber is a ost-doctoral Research Fellow at the Mannheim Centre for European Social Research (MZES) at the University of Mannheim.

Model Card Contact

For questions about this model, please open an issue in the repository or contact the lead author at rwhorne@clemson.edu.

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