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
| { | |
| "fileFormatVersion": "1.0.0", | |
| "itemInfoEntries": { | |
| "4BDEEC23-5067-410C-8A8F-A649FD4360D9": { | |
| "author": "com.apple.CoreML", | |
| "description": "CoreML Model Specification", | |
| "name": "model.mlmodel", | |
| "path": "com.apple.CoreML/model.mlmodel" | |
| }, | |
| "B7AAB529-A51F-4EB3-B2CD-4BDA80250E6F": { | |
| "author": "com.apple.CoreML", | |
| "description": "CoreML Model Weights", | |
| "name": "weights", | |
| "path": "com.apple.CoreML/weights" | |
| } | |
| }, | |
| "rootModelIdentifier": "4BDEEC23-5067-410C-8A8F-A649FD4360D9" | |
| } | |