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:
- 3cd0840c22ddb39f2d269801fb65cc4b58e1a60270a8ae8ffa1d71e2848cde28
- Size of remote file:
- 499 MB
- SHA256:
- b8f5c1edb76303d829a46855c5f1abc9b354748a4945618f17d3fc0d5898b93f
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