| --- |
| license: cc-by-2.0 |
| datasets: |
| - CreativeLang/vua20_metaphor |
| language: |
| - en |
| --- |
| |
| # Metaphor_Detection_Roberta_Seq |
| |
| ## Description |
| |
| - **Paper:** [FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning](https://aclanthology.org/2023.eacl-main.114.pdf) |
| |
| ## 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](https://huggingface.co/datasets/CreativeLang/vua20_metaphor) dataset. |
| |
| To use this model, please use the `inference.py` in the [FrameBERT repo](https://github.com/liyucheng09/MetaphorFrame). |
| |
| 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](https://aclanthology.org/2023.eacl-main.114.pdf). |
| |
| ## 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](mailto:yucheng.li@surrey.ac.uk). |
