Feature Extraction
Transformers
Safetensors
English
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use lucas-lage/ReSB2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lucas-lage/ReSB2-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lucas-lage/ReSB2-Base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lucas-lage/ReSB2-Base") model = AutoModel.from_pretrained("lucas-lage/ReSB2-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcc606357620161d3b26bc132d9a8627e7087ae1214d0f0a98726235e7bdd0d0
- Size of remote file:
- 34.4 MB
- SHA256:
- a372bdc5505e679502b4ff864e9b4df498cc6494cd2c582ddd7674453941890a
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