Fill-Mask
Transformers
PyTorch
English
modernbert
ettin
encoder
text-embeddings
retrieval
classification
Instructions to use jhu-clsp/ettin-encoder-150m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/ettin-encoder-150m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jhu-clsp/ettin-encoder-150m")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-encoder-150m") model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/ettin-encoder-150m", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 750af76f6701c5a729a657bccb44f7768f3fb7ef459cedd3ea080c0396eef597
- Size of remote file:
- 599 MB
- SHA256:
- fd77a88ef9599bf8698811e3d6010fc1c4a48bf2149cd56d49b146469b394754
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