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CreativeLang/metaphor_detection_roberta_seq
metaphor_detection_roberta_seq is a token classification model from CreativeLang. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-2.0.
- Paper: FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
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From the Hugging Face model README
Creative Language Toolkit (CLTK) Metadata
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.
| 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 |
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}
}
If you have any queries, please open an issue or direct your queries to mail.