identrics/wasper_propaganda_detection_en

Model Card for identrics/wasperpropagandadetection_en

Model Description

• —Developed by: `Identrics`
• —Language: English
• —License: apache-2.0
• —Finetuned from model: `google-bert/bert-base-cased`
• —Context window : 512 tokens

Model Description

This model consists of a fine-tuned version of google-bert/bert-base-cased for a propaganda detection task. It is effectively a binary classifier, determining whether propaganda is present in the output string. This model was created by `Identrics`, in the scope of the WASPer project. The detailed taxonomy of the full pipeline could be found here.

Uses

Designed as a binary classifier to determine whether a traditional or social media comment contains propaganda.

Example

First install direct dependencies:

pip install transformers torch accelerate

Then the model can be downloaded and used for inference:

from transformers import pipeline

labels_map = {"0": "No Propaganda", "1": "Propaganda"}

pipe = pipeline(
    "text-classification",
    model="identrics/wasper_propaganda_detection_en",
    tokenizer="identrics/wasper_propaganda_detection_en",
)

text = "Our country is the most powerful country in the world!"

prediction = pipe(text)
print(labels_map[prediction[0]["label"]])

Training Details

The training dataset for the model consists of a balanced collection of English examples, including both propaganda and non-propaganda content. These examples were sourced from a variety of traditional media and social media platforms and manually annotated by domain experts. Additionally, the dataset is enriched with AI-generated samples.

The model achieved an F1 score of 0.807 during evaluation.

Compute Infrastructure

This model was fine-tuned using a GPU / 2xNVIDIA Tesla V100 32GB.

Citation [this section is to be updated soon]

If you find our work useful, please consider citing WASPer:

@article{...2024wasper,
  title={WASPer: Propaganda Detection in Bulgarian and English}, 
  author={....},
  journal={arXiv preprint arXiv:...},
  year={2024}
}