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README.md

@@ -13,12 +13,33 @@ tags:
 - RoBERTa-base
 ---
 
+# Model for predicting MediaFrames on arguments
+
 A model for predicting a subset of MediaFrames given an argument (has not to be structured in premise/ conclusion or something else). To investigate the generic frame classes, have a look at [The Media Frames Corpus: Annotations of Frames Across Issues](https://aclanthology.org/P15-2072/)
 
 Also, this model was fine-tuned on the data provided by [this paper](https://aclanthology.org/P15-2072/). To be precise, we did the following:
 
 > To apply these frames to arguments from DDO, we fine-tune a range of classifiers on a comprehensive training dataset of more than 10,000 newspaper articles that discuss immigration, same-sex marriage, and marijuana, containing 146,001 labeled text spans labeled with a single MediaFrame-class per annotator. To apply this dataset to our argumentative domain, we broaden the annotated spans to sentence level (see [here](https://www.degruyter.com/document/doi/10.1515/itit-2020-0054/html)). Since an argument can address more than a single frame, we design the argument-frame classification task as a multi-label problem by combining all annotations for a sentence into a frame target set. In addition, to broaden the target frame sets, we create new instances merging two instances by combining their textual representation and unifying their target frame set.
 
+## Used arguments for fine-tuning
+
+````txt
+per_device_train_batch_size=16,
+per_device_eval_batch_size=64,
+group_by_length=False,
+evaluation_strategy="epoch",
+num_train_epochs=5,
+save_strategy="epoch",
+load_best_model_at_end=True,
+save_total_limit=3,
+metric_for_best_model="eval_macro avg -> f1-score",
+greater_is_better=True,
+learning_rate=5e-5,
+warmup_ratio=0.1
+````
+
+## Performance
+
 On the test split of this composed dataset, we measure the following performances:
 
 ````txt