---
language: 
  - sk
license: apache-2.0
tags:
  - hate-speech
  - text-cleaning
  - slovak
  - t5
  - transformer
datasets:
  - None
metrics:
  - accuracy
pipeline_tag: text2text-generation
---
# Model Card: SlovHC – Slovak Hate Speech Corrector

## Model Overview

**SlovHC** is a fine-tuned model designed specifically for correcting hate speech in the Slovak language. Given the limited availability of robust language models for low-resource languages like Slovak, SlovHC aims to fill this gap by offering high-quality performance in identifying and masking offensive content.

## Key Features

* Tailored for the Slovak language
* Focuses on masking hate speech while preserving sentence structure
* Utilizes pre-trained Slovak BERT tokenizer for consistent tokenization

## Example Outputs

**Input:** Ty si absolútny magor.  
**Output:** Ty si absolútny \*\*\*\*\*.

---

**Input:** Priblblé električky stále meškajú.  
**Output:** \*\*\*\*\*\*\*\*\* električky stále meškajú.

---

**Input:** Opač jak ši sebe obľik tote nohavky, ši jak mantak.  
**Output:** Opač jak ši sebe obľik tote nohavky, ši jak \*\*\*\*\*\*.

## Tokenizer

We did not develop a new tokenizer for this model. Instead, we leveraged the high-quality tokenizer provided by [`gerulata/slovakbert`](https://huggingface.co/gerulata/slovakbert), which aligns well with our model’s requirements.

## How to Use

Here's a simple example demonstrating how to load and run inference with the model:

```python
from transformers import RobertaTokenizer, AutoModelForSeq2SeqLM

# Load pretrained tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained(
    'gerulata/slovakbert',
    weights_only=False,
    token="###YOUR_HF_TOKEN###"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "timotejKralik/hate_speech_correction_slovak",
    weights_only=False,
    token="###YOUR_HF_TOKEN###"
)

# Input text containing potentially harmful language
input_text = "Opač jak ši sebe obľik tote nohavky, ši jak mantak."
print(f"Input: {input_text}")

# Tokenize input and generate output
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Output:", output_text)
```