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