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
- Xet hash:
- 77e394bde888255023b0f4d04cc779f405e29b2f7e750455ba60b7fd4f46a1c5
- Size of remote file:
- 499 MB
- SHA256:
- 9c50a79006a36126f481a310038bcc2e4d6f049af0ff8c08910f9d977af3679e
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