Instructions to use IMSyPP/hate_speech_slo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IMSyPP/hate_speech_slo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IMSyPP/hate_speech_slo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IMSyPP/hate_speech_slo") model = AutoModelForSequenceClassification.from_pretrained("IMSyPP/hate_speech_slo") - Notebooks
- Google Colab
- Kaggle
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README.md
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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.
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## Tokenizer
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During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.
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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.
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## Please cite:
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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.
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https://link.springer.com/chapter/10.1007/978-3-031-08974-9_54
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## Tokenizer
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During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.
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