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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af4d22323525f525378d7f19c4e2169ff197ecddd10149d7fe2a84c3e4e07449
- Size of remote file:
- 595 MB
- SHA256:
- 0479301d5a26adc50901335a2139018f2f5958d791ab53058424d4bc879098d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.