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
File size: 617 Bytes
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