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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36e91228d92c7177034989c83f1b6cc1efca68f2e0fd72b8d8574580be784849
- Size of remote file:
- 438 MB
- SHA256:
- 51d45cb59176970c55cfbe4f91c8ba1c98baa944c26a93686780f2e6ef66c408
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