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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cee7aa9516a7405be3d695db117e1236dafa893c1a686141b2e7f59aefebafc1
- Size of remote file:
- 134 Bytes
- SHA256:
- 343b780e7ffb271a8f6405f2aef1b84bda311dc81f2f0724ab04205c3b49848b
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