Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
English
bert
mteb
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use vectoriseai/ember-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vectoriseai/ember-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vectoriseai/ember-v1") 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
- Xet hash:
- db1486abbbeed6cc3d1d925e71baaa6342342979fa746461f0e3e3807da35a75
- Size of remote file:
- 1.34 GB
- SHA256:
- 63cd396b456f848b7e643f3d6a703a01c7b08337519ee1bf5accbc12c8ea1998
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.