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README.md

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 license: cc-by-nc-sa-3.0
+metrics:
+- f1
+- accuracy
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+- text: "The price keeps rising."
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+# Multilingual-Metaphor-Detection
+
+This page provides a fine-tuned multilingual language model [XLM-RoBERTa](https://arxiv.org/pdf/1911.02116.pdf) for metaphor detection on a token-level using [Huggingface](https://huggingface.co/tasks/token-classification).
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+# Dataset
+The dataset the model is trained on is the [VU Amsterdam Metaphor Corpus](http://www.vismet.org/metcor/documentation/home.html) 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](https://competitions.codalab.org/competitions/22188#results) our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-R<sub>Base</sub> and 0.77 when training XLM-R<sub>Large.</sub>. 
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+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](https://github.com/lwachowiak/Multilingual-Metaphor-Detection/edit/main/README.md)