---
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`](https://identrics.ai/)
- **Language:** English
- **License:** apache-2.0
- **Finetuned from model:** [`google-bert/bert-base-cased`](https://huggingface.co/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`](https://identrics.ai/), 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:
```py
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: 


![image/png](https://cdn-uploads.huggingface.co/production/uploads/66741cdd8123010b8f63f965/aGE-QqyzAN-zB13f8z3mQ.png)

The dataset is divided in proportions 80/10/10 for training, testing and evaluation respectively. After fine-tuning on the training dataset, 


We achieved an f1 score of 0.807 on evaluation dataset.






- PEFT 0.11.1