Feature Extraction
Transformers
PyTorch
English
modernbert
fill-mask
encoder
text-embeddings-inference
Instructions to use jhu-clsp/ettin-encoder-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/ettin-encoder-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jhu-clsp/ettin-encoder-1b")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-encoder-1b") model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/ettin-encoder-1b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#4 opened about 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#1 opened about 1 year ago
by
SFconvertbot