Instructions to use HeNLP/HeRo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HeNLP/HeRo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HeNLP/HeRo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HeNLP/HeRo") model = AutoModelForMaskedLM.from_pretrained("HeNLP/HeRo") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- he
datasets:
- HeNLP/HeDC4
Hebrew Language Model
State-of-the-art RoBERTa language model for Hebrew.
How to use
from transformers import AutoModelForMaskedLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('HeNLP/HeRo')
model = AutoModelForMaskedLM.from_pretrained('HeNLP/HeRo')
Citing
If you use HeRo in your research, please cite HeRo: RoBERTa and Longformer Hebrew Language Models.
@article{shalumov2023hero,
title={HeRo: RoBERTa and Longformer Hebrew Language Models},
author={Vitaly Shalumov and Harel Haskey},
year={2023},
journal={arXiv:2304.11077},
}