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
File size: 726 Bytes
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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 |