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identrics/wasper_propaganda_detection_bg
wasper_propaganda_detection_bg is a machine learning model from identrics. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
- Developed by: Identrics - Language: Bulgarian - License: apache-2.0 - Finetuned from model: INSAIT-Institute/BgGPT-7B-Instruct-v0.2 - Context window : 8192 tokens
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From the Hugging Face model README
IdentricsINSAIT-Institute/BgGPT-7B-Instruct-v0.2This model consists of a fine-tuned version of BgGPT-7B-Instruct-v0.2 for a propaganda detection task. It is effectively a binary classifier, determining wether 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.
Designed as a binary classifier to determine whether a traditional or social media comment contains propaganda.
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 = {"LABEL_0": "No Propaganda", "LABEL_1": "Propaganda"}
pipe = pipeline(
"text-classification",
model="identrics/wasper_propaganda_detection_bg",
tokenizer="identrics/wasper_propaganda_detection_bg",
)
text = "Газа евтин, американското ядрено гориво евтино, пълно с фотоволтаици а пък тока с 30% нагоре. Защо ?"
prediction = pipe(text)
print(labels_map[prediction[0]["label"]])
The training dataset for the model consists of a balanced collection of Bulgarian 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 0.836 during evaluation.
This model was fine-tuned using a GPU / 2xNVIDIA Tesla V100 32GB.
If you find our work useful, please consider citing WASPer:
@article{2025wasper,
title={WASPER: A BULGARIAN-LANGUAGE MODEL FOR DETECTING PROPAGANDA IN SOCIAL MEDIA CONTENT},
author={Petrova, Y., Kiryakov, T., Kotseva, D. & Kostadinova, B.},
journal={THE EUROPEAN UNION IN A DYNAMIC GLOBAL CONTEXT},
year={2025}
}