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:
- e38b861605495609a8f58ebfbccc72d87ab1c076eefff3000ef1e03c2c8b5336
- Size of remote file:
- 134 Bytes
- SHA256:
- 23d64a63557d28f6b25b00b6d5a80a0e72d3f4313b6ba93bb5f4f9bed438a83e
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