Feature Extraction
sentence-transformers
ONNX
Safetensors
xlm-roberta
sentence-similarity
dense-encoder
dense
retrieval
multimodal
multi-modal
crossmodal
cross-modal
aerospace
telepix
text-embeddings-inference
Instructions to use telepix/PIXIE-Rune-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use telepix/PIXIE-Rune-v1.0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("telepix/PIXIE-Rune-v1.0") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download onnx/model.onnx_data from telepix/PIXIE-Rune-v1.0: direct link, hf CLI and curl.
- Browser
- Download file 2.27 GB
-
https://huggingface.co/telepix/PIXIE-Rune-v1.0/resolve/main/onnx/model.onnx_data
- Command line
-
hf download hf://telepix/PIXIE-Rune-v1.0/onnx/model.onnx_data
-
curl -L -o model.onnx_data https://huggingface.co/telepix/PIXIE-Rune-v1.0/resolve/main/onnx/model.onnx_data
2.27 GB
- Xet hash:
- be225db2accc25c23b99ec94343971fc9199e0d66099468d92fcf1ba7fd1d3f0
- Size of remote file:
- 2.27 GB
- SHA256:
- 84d64606ff97e4abfbac9b03dc713c1fe3de955a66ea9f46c20c6a12d5f0512b
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