README.md

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

# Model Card for identrics/wasper_propaganda_classifier_bg




## Model Description

- **Developed by:** [`Identrics`](https://identrics.ai/)
- **Language:** Bulgarian
- **License:** apache-2.0
- **Finetuned from model:** [`INSAIT-Institute/BgGPT-7B-Instruct-v0.2`](https://huggingface.co/INSAIT-Institute/BgGPT-7B-Instruct-v0.2)
- **Context window :** 8192 tokens

## Model Description

This model consists of a fine-tuned version of BgGPT-7B-Instruct-v0.2 for a propaganda detection task. It is effectively a multilabel classifier, determining wether a given propaganda text in Bulgarian contains or not 5 predefined propaganda types.
This model was created by [`Identrics`](https://identrics.ai/), in the scope of the WASPer project. The detailed taxonomy could be found [here](https://github.com/Identrics/wasper/).


## Propaganda taxonomy

The propaganda techniques we want to identify are classified in 5 categories:

1. **Self-Identification Techniques**:
These techniques exploit the audience's feelings of association (or desire to be associated) with a larger group. They suggest that the audience should feel united, motivated, or threatened by the same factors that unite, motivate, or threaten that group.


2. **Defamation Techniques**:
These techniques represent direct or indirect attacks against an entity's reputation and worth.

3. **Legitimisation Techniques**:
These techniques attempt to prove and legitimise the propagandist's statements by using arguments that cannot be falsified because they are based on moral values or personal experiences.

4. **Logical Fallacies**:
These techniques appeal to the audience's reason and masquerade as objective and factual arguments, but in reality, they exploit distractions and flawed logic.

5. **Rhetorical Devices**:
These techniques seek to influence the audience and control the conversation by using linguistic methods.




## Uses

To be used as a multilabel classifier to identify if the Bulgarian sample text contains one or more of the five propaganda techniques mentioned above.
### Example

First install direct dependencies:
```
pip install transformers torch accelerate
```

Then the model can be downloaded and used for inference:
```py
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

labels = [
    "Legitimisation Techniques",
    "Rhetorical Devices",
    "Logical Fallacies",
    "Self-Identification Techniques",
    "Defamation Techniques",
]

model = AutoModelForSequenceClassification.from_pretrained(
    "identrics/wasper_propaganda_classifier_bg", num_labels=5
)
tokenizer = AutoTokenizer.from_pretrained("identrics/wasper_propaganda_classifier_bg")

text = "Газа евтин, американското ядрено гориво евтино, пълно с фотоволтаици а пък тока с 30% нагоре. Защо ?"

inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

probabilities = torch.sigmoid(logits).cpu().numpy().flatten()

# Format predictions
predictions = {labels[i]: probabilities[i] for i in range(len(labels))}
print(predictions)
```


## Training Details


During the training stage, the objective was to develop the multi-label classifier to identify different types of propaganda using a dataset containing both real and artificially generated samples.

The data has been carefully annotated by domain experts based on a predefined taxonomy, which covers five primary categories. Some examples are assigned to a single category, while others are classified into multiple categories, reflecting the nuanced nature of propaganda where multiple techniques can  be found within a single text.


The model reached an F1-weighted score of **0.538** during training.
 
## 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}
}
```