metaphor-cat-mdeberta-no-weights

metaphor-cat-mdberta-no-weights

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the Catalan metaphor detection dataset metaphor-catalan.

It achieves the following results on the evaluation set:

• Loss: 0.1220
• Precision: 0.75
• Recall: 0.4286
• F1: 0.5455
• Accuracy: 0.9641

Model description

This model is a DeBERTa-based transformer fine-tuned for token-level metaphor detection in Catalan. It predicts whether each token in a sentence belongs to a metaphorical expression. The model is suitable for research and applications in computational linguistics, natural language understanding, and figurative language detection in Catalan text.

Intended uses & limitations

Intended uses:

• Detecting metaphors in Catalan text.
• Assisting linguistic research on figurative language.
• Integrating metaphor detection into NLP pipelines for Catalan text analysis.

Limitations:

• The dataset is relatively small and may not cover all domains or metaphorical expressions.
• Recall is moderate (0.4286), meaning the model may miss some metaphors.
• The dataset is imbalanced, with many more literal tokens than metaphorical ones.
• Not suitable for critical decision-making applications without further evaluation.

Training and evaluation data

Training dataset: metaphor-catalan

• Contains Catalan sentences annotated for metaphor detection at the token level.

Training procedure

Hyperparameters

• Learning rate: 2e-05
• Train batch size: 8
• Evaluation batch size: 8
• Optimizer: AdamW (fused, betas=(0.9,0.999), epsilon=1e-08)
• LR scheduler: linear
• Epochs: 5
• Mixed precision: Native AMP

Training results

Framework versions

• Transformers: 4.57.3
• PyTorch: 2.9.0+cu126
• Datasets: 4.0.0
• Tokenizers: 0.22.1