Instructions to use SamuelYang/SentMAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamuelYang/SentMAE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SamuelYang/SentMAE")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SamuelYang/SentMAE") model = AutoModelForMaskedLM.from_pretrained("SamuelYang/SentMAE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff402c4462358e9c9c2007a8a4c3e2293c0d3374c04798f3068b046cd42eb399
- Size of remote file:
- 438 MB
- SHA256:
- 50ec426a47e4ddb1cc761925b2ae57a14276d7ddd85d90815c46c6d065342857
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