Metaphor_Detection_Roberta_Seq

Description

• Paper: FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning

Model Summary

Creative Language Toolkit (CLTK) Metadata

• CL Type: Metaphor
• Task Type: detection
• Size: roberta-base (500MB)
• Created time: 2022

This model is a easy to use metaphor detection baseline realised with roberta-base fine-tuned on CreativeLang/vua20_metaphor dataset.

To use this model, please use the inference.py in the FrameBERT repo.

Just run:

python inference.py CreativeLang/metaphor_detection_roberta_seq

Check out inference.py to learn how to apply the model on your own data.

For the details of this model and the dataset used, we refer you to the release paper.

Metrics

Metric	Value
eval_loss	0.2656
eval_accuracy_score	0.9142
eval_precision	0.9142
eval_recall	0.9142
eval_f1	0.9142
eval_f1_macro	0.7315
eval_runtime	8.9802
eval_samples_per_second	411.7960
eval_steps_per_second	51.5580
epoch	3.0000

Citation Information

If you find this dataset helpful, please cite:

@article{Li2023FrameBERTCM,
  title={FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning},
  author={Yucheng Li and Shunyu Wang and Chenghua Lin and Frank Guerin and Lo{\"i}c Barrault},
  journal={ArXiv},
  year={2023},
  volume={abs/2302.04834}
}

Contributions

If you have any queries, please open an issue or direct your queries to mail.