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
metadata
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: