Update README.md

README.md

@@ -9,101 +9,52 @@ base_model:
 pipeline_tag: text-classification
 ---
 
+This model is a multilingual sentence-level media bias classifier.
 
-This model is a version of [mediabiasgrouup/magpie-pt-xlm](https://huggingface.co/mediabiasgroup/magpie-pt-xlm), fine-tuned on BABE dataset for sentence-level media bias binary classification.
-The model is based on [model architecture, e.g., BERT, RoBERTa, etc.] and has been fine-tuned on [mention the dataset] for [number of epochs or other training details].
-
+It is a version of [mediabiasgrouup/magpie-pt-xlm](https://huggingface.co/mediabiasgroup/magpie-pt-xlm), fine-tuned for a media bias classification.
+It has been pre-trained on LBM (Large Bias Mixture) collection of 59 tasks and then fine-tuned on the [mediabiasgrouup/BABE](https://huggingface.co/mediabiasgroup/BABE) dataset.
 
 ---
 
 ## Training details
 
-- **Base Model:** FacebookAI/xlm-roberta-base
+- **Base Model:** mediabiasgroup/magpie-pt-xlm
 - **Number of Parameters:** 279M
 - **Max Sequence Length:** 128
 
-### Training Data
-
-The model was fine-tuned on the [name of dataset] dataset. This dataset consists of [short description of dataset, e.g., number of instances, labels, any important data characteristics].
-
-You can find the dataset [here](dataset_url).
-
----
-
-## Evaluation Results
-
-The model was evaluated on [mediabiasgroup/BABE] test set and achieved the following results:
-
-- **Accuracy:** [accuracy score]  
-- **F1-Score:** [F1 score]  
-- **Precision:** [precision score]  
-- **Recall:** [recall score]  
-
-For detailed evaluation results, see the corresponding paper.
-
----
-
-## Usage
-
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("mediabiasgroup/magpie-babe-ft-xlm")
-model = AutoModelForSequenceClassification.from_pretrained("mediabiasgroup/magpie-babe-ft-xlm")
-
-# Example input
-input_text = "Your example sentence goes here."
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model(**inputs)
-
-# Accessing the predicted class
-predicted_class = outputs.logits.argmax(dim=-1)
-print(f"Predicted class: {predicted_class}")
-```
-
----
-
-## Example Code
-
-Here’s an example for batch classification:
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer = AutoTokenizer.from_pretrained("your_org/your_model")
-model = AutoModelForSequenceClassification.from_pretrained("your_org/your_model")
-
-# Example sentences
-sentences = ["Sentence 1", "Sentence 2", "Sentence 3"]
-inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
-
-with torch.no_grad():
-    outputs = model(**inputs)
-
-predicted_classes = outputs.logits.argmax(dim=-1)
-print(f"Predicted classes: {predicted_classes}")
-```
-
 ---
 
 ## Citation
 
 The code for the training is available at: https://github.com/Media-Bias-Group/magpie-multi-task
+The paper is avalable at: https://aclanthology.org/2024.lrec-main.952/ 
 If you use this model, please cite the following paper(s):
 
 ```bibtex
-@article{your_citation,
-  title={Your Title},
-  author={Your Name and Co-authors},
-  journal={Journal Name},
-  year={Year},
-  publisher={Publisher},
-  url={paper_url}
+@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.",
 }
-```
-
----
-
-Feel free to adapt this template to match the specific needs of each model. Let me know if you'd like to adjust any sections further!
+```