metadata

base_model: INSAIT-Institute/BgGPT-7B-Instruct-v0.2
library_name: peft
license: apache-2.0
language:
  - en
tags:
  - propaganda

Model Card for identrics/BG_propaganda_detector

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 wether propaganda is present in the output string. This model was created by Identrics, in the scope of the WASPer project.

Uses

To be used as a binary classifier to identify if propaganda is present in a string containing a comment from a social media site

Example

First install direct dependencies:

pip install transformers torch accelerate

Then the model can be downloaded and used for inference:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("identrics/EN_propaganda_detector", num_labels=2)
tokenizer = AutoTokenizer.from_pretrained("identrics/EN_propaganda_detector")

tokens = tokenizer("Our country is the most powerful country in the world!", return_tensors="pt")
output = model(**tokens)
print(output.logits)

Training Details

The training datasets for the model consist of a balanced set totaling 840 English examples that include both propaganda and non-propaganda content. These examples are collected from a variety of traditional media and social media sources, ensuring a diverse range of content. Aditionally, the training dataset is enriched with AI-generated samples. The total distribution of the training data is shown in the table below:

The model was then tested on a smaller evaluation dataset, achieving an f1 score of 0.807.

Citation

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

@article{bai2024longwriter,
  title={LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs}, 
  author={Yushi Bai and Jiajie Zhang and Xin Lv and Linzhi Zheng and Siqi Zhu and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li},
  journal={arXiv preprint arXiv:2408.07055},
  year={2024}
}

PEFT 0.11.1