
# Hate Speech Classifier for Social Media Content in Slovenian Language

A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model.

## Please cite:
Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.
https://link.springer.com/chapter/10.1007/978-3-031-08974-9_54

## Tokenizer

During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.

## Model output

The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent
{"language": ["sl"], "license": "mit", "pipeline_tag": "text-classification", "inference": true, "widget": [{"text": "Sem Mark in \u017eivim v Ljubljani. Sem doktorski \u0161tudent na Mednarodni podiplomski \u0161oli Jo\u017eefa Stefana."}]}
transformers pytorch bert text-classification sl license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #sl #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Hate Speech Classifier for Social Media Content in Slovenian Language

A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model.

## Please cite:
Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.
URL

## Tokenizer

During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.

## Model output

The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent
# Hate Speech Classifier for Social Media Content in Slovenian Language

A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model. ## Please cite:
Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.
URL ## Tokenizer

During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference. ## Model output

The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent
TAGS
#transformers #pytorch #bert #text-classification #sl #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Hate Speech Classifier for Social Media Content in Slovenian Language

A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model. ## Please cite:
Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.
URL ## Tokenizer

During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference. ## Model output

The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent
passage: TAGS
#transformers #pytorch #bert #text-classification #sl #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Hate Speech Classifier for Social Media Content in Slovenian Language

A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model.## Please cite:
Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.
URL## Tokenizer

During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.## Model output

The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent