Text Classification
Transformers
PyTorch
Hebrew
bert
feature-extraction
code
text-embeddings-inference
Instructions to use SinaLab/Offensive-Hebrew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SinaLab/Offensive-Hebrew with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SinaLab/Offensive-Hebrew")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SinaLab/Offensive-Hebrew") model = AutoModel.from_pretrained("SinaLab/Offensive-Hebrew", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-sa-4.0 | |
| language: | |
| - he | |
| metrics: | |
| - accuracy | |
| pipeline_tag: text-classification | |
| tags: | |
| - code | |
| ## Hebrew Corpus | |
| This corpus contains offensive language in Hebrew manually annotated. The data includes 15,881 tweets, labeled with one or more of five classes (abusive, hate, violence, pornographic, or non-offensive). The corpus is annonated manually by Arabic-Hebrew bilingual speakers. | |
| https://arxiv.org/abs/2309.02724 | |
| ## Models | |
| AlephBERT (https://huggingface.co/imvladikon/sentence-transformers-alephbert) | |
| ## Github Repository | |
| git clone https://github.com/SinaLab/OffensiveHebrew | |
| You can download the data from the following GitGub link: | |
| https://github.com/SinaLab/OffensiveHebrew/tree/main/data |