Feature Extraction
Transformers
PyTorch
Core ML
ONNX
Safetensors
bert
fill-mask
custom_code
text-embeddings-inference
Instructions to use Severian/embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Severian/embed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Severian/embed", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Severian/embed", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("Severian/embed", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30a82b2a93beedc52e7357c491071265cea61461f3b8c6e30ff7f093b0c5a44d
- Size of remote file:
- 131 Bytes
- SHA256:
- 73ca7d3f974d84539afbec0492857f557a587f72f00f3f22e822fe514cdd9812
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.