Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+language:
+- multilingual
+base_model:
+- FacebookAI/xlm-roberta-base
+pipeline_tag: text-classification
+---
+
+This is a model pre-trained on LBM (Large Bias Mixture) collection of 59 bias-related tasks in via multi-task learning.
+---
+
+## Citation
+
+**Code repository**: https://github.com/Media-Bias-Group/magpie-multi-task
+
+**Paper**: https://aclanthology.org/2024.lrec-main.952/
+
+
+If you use this model, please cite the following paper(s):
+
+```bibtex
+@inproceedings{horych-etal-2024-magpie,
+    title = "{MAGPIE}: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions",
+    author = "Horych, Tom{\'a}{\v{s}}  and
+      Wessel, Martin Paul  and
+      Wahle, Jan Philip  and
+      Ruas, Terry  and
+      Wa{\ss}muth, Jerome  and
+      Greiner-Petter, Andr{\'e}  and
+      Aizawa, Akiko  and
+      Gipp, Bela  and
+      Spinde, Timo",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.952",
+    pages = "10903--10920",
+    abstract = "Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To address this, we introduce MAGPIE, a large-scale multi-task pre-training approach explicitly tailored for media bias detection. To enable large-scale pre-training, we construct Large Bias Mixture (LBM), a compilation of 59 bias-related tasks. MAGPIE outperforms previous approaches in media bias detection on the Bias Annotation By Experts (BABE) dataset, with a relative improvement of 3.3{\%} F1-score. Furthermore, using a RoBERTa encoder, we show that MAGPIE needs only 15{\%} of fine-tuning steps compared to single-task approaches. We provide insight into task learning interference and show that sentiment analysis and emotion detection help learning of all other tasks, and scaling the number of tasks leads to the best results. MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models. Furthermore, LBM is the first available resource collection focused on media bias MTL.",
+}
+```