YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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, 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:

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)

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