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:
- 1eb0193f2aa6c4c23b1474d96ad2d1b84f3ddc607978e60e49ae2c03f9a50b17
- Size of remote file:
- 595 MB
- SHA256:
- 02fa7ad0d042d78ca6cfb96cf2d1cec5222a1f11cd5c9d1c7c0a5040a1d33ee1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.