license: cc-by-nc-sa-3.0
metrics:
- f1
- accuracy
widget:
- text: The price keeps rising.
Multilingual-Metaphor-Detection
This page provides a fine-tuned multilingual language model XLM-RoBERTa for metaphor detection on a token-level using Huggingface.
Dataset
The dataset the model is trained on is the VU Amsterdam Metaphor Corpus that was annotated on a word-level following the metaphor identification protocol. The training corpus is restricted to English, however, XLM-R shows decent zero-shot performances when tested on other languages.
Results
Following the evaluation criteria from the 2020 Second Shared Task on Metaphor detection our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-RBase and 0.77 when training XLM-RLarge..
We train for 8 epochs loading the model with the best evaluation performance at the end and using a learning rate of 2e-5. From the allocated training data 10% are utilized for validation while the final test set is being kept seperate and only used for the final evaluation.
Code for Training
The training and evaluation code is available on Github