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:
- f2ae573ffa444bcc69062f6054fec18047b47b220bea6fa685ed7ae5643a75e7
- Size of remote file:
- 330 MB
- SHA256:
- bc05cfa7c15d0bebe2ceef5fb98982d63ed32f853e1139b63fb78ae09ae49895
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.