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
| 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}, | |
| } | |
| ``` |