Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
dense-encoder
dense
telepix
text-embeddings-inference
Instructions to use telepix/PIXIE-Rune-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use telepix/PIXIE-Rune-Preview with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("telepix/PIXIE-Rune-Preview") 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -170,4 +170,4 @@ for query, query_scores in zip(queries, scores):
|
|
| 170 |
|
| 171 |
## Contact
|
| 172 |
|
| 173 |
-
If you have any suggestions or questions about
|
|
|
|
| 170 |
|
| 171 |
## Contact
|
| 172 |
|
| 173 |
+
If you have any suggestions or questions about the PIXIE, please reach out to the authors at bmkim@telepix.net.
|