Instructions to use Hikam22/Review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hikam22/Review with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Hikam22/Review")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hikam22/Review") model = AutoModelForMaskedLM.from_pretrained("Hikam22/Review") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c747f6dec277cb52761be9dca7e7a9a0c0ee18c629da554f82939abc73da03e8
- Size of remote file:
- 1.02 kB
- SHA256:
- 389349734a704424ef4fd01e2674ed03abd53c8a0dedc709ca559d46ccf4c1dc
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