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--- |
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license: cc-by-2.0 |
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datasets: |
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- CreativeLang/vua20_metaphor |
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language: |
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- en |
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--- |
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# Metaphor_Detection_Roberta_Seq |
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## Description |
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- **Paper:** [FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning](https://aclanthology.org/2023.eacl-main.114.pdf) |
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## Model Summary |
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Creative Language Toolkit (CLTK) Metadata |
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- CL Type: Metaphor |
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- Task Type: detection |
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- Size: roberta-base (500MB) |
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- Created time: 2022 |
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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. |
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To use this model, please use the `inference.py` in the [FrameBERT repo](https://github.com/liyucheng09/MetaphorFrame). |
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Just run: |
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``` |
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python inference.py CreativeLang/metaphor_detection_roberta_seq |
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``` |
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Check out `inference.py` to learn how to apply the model on your own data. |
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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). |
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## Metrics |
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| Metric | Value | |
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|----------------------------------|--------------------------| |
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| eval_loss | 0.2656 | |
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| eval_accuracy_score | 0.9142 | |
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| eval_precision | 0.9142 | |
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| eval_recall | 0.9142 | |
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| eval_f1 | 0.9142 | |
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| eval_f1_macro | 0.7315 | |
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| eval_runtime | 8.9802 | |
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| eval_samples_per_second | 411.7960 | |
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| eval_steps_per_second | 51.5580 | |
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| epoch | 3.0000 | |
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### Citation Information |
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If you find this dataset helpful, please cite: |
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``` |
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@article{Li2023FrameBERTCM, |
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title={FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning}, |
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author={Yucheng Li and Shunyu Wang and Chenghua Lin and Frank Guerin and Lo{\"i}c Barrault}, |
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journal={ArXiv}, |
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year={2023}, |
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volume={abs/2302.04834} |
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} |
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``` |
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### Contributions |
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If you have any queries, please open an issue or direct your queries to [mail](mailto:yucheng.li@surrey.ac.uk). |
