Instructions to use jhu-clsp/ettin-encoder-68m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/ettin-encoder-68m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jhu-clsp/ettin-encoder-68m")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-encoder-68m") model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/ettin-encoder-68m") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 997af021c414892904896e5714783ea5d95c83ddd752615df738e47e2030fb3d
- Size of remote file:
- 274 MB
- SHA256:
- 97ef650cbe35e69c363f28edbbc30be68912130d6a909de3a05714bd7bb1217d
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