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---
language:
- en
metrics:
- f1
pipeline_tag: text-classification
tags:
- classification
- framing
- MediaFrames
- argument classification
- multilabel
- 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
"test_macro avg -> f1-score": 0.7323500703250138,
"test_macro avg -> precision": 0.7240108073952866,
"test_macro avg -> recall": 0.7413112856192988,
"test_macro avg -> support": 27705,
"test_micro avg -> f1-score": 0.7956475205137353,
"test_micro avg -> precision": 0.7865279492153059,
"test_micro avg -> recall": 0.804981050351922,
"test_micro avg -> support": 27705,
```` |