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 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. The evaluation dataset is distributed as such:
- PEFT 0.11.1