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
|
@@ -8,8 +8,11 @@ pipeline_tag: sentence-similarity
|
|
| 8 |
library_name: sentence-transformers
|
| 9 |
---
|
| 10 |
|
| 11 |
-
# PIXIE-Rune
|
| 12 |
An encoder-based embedding model trained on Korean and English triplets, developed by [TelePIX Co., Ltd](https://telepix.net/).
|
|
|
|
|
|
|
|
|
|
| 13 |
|
| 14 |
## Model Details
|
| 15 |
|
|
|
|
| 8 |
library_name: sentence-transformers
|
| 9 |
---
|
| 10 |
|
| 11 |
+
# PIXIE-Rune-M-v1.0
|
| 12 |
An encoder-based embedding model trained on Korean and English triplets, developed by [TelePIX Co., Ltd](https://telepix.net/).
|
| 13 |
+
**PIXIE-Rune-M-v1.0** is a multilingual model specifically optimized for Korean and English.
|
| 14 |
+
It demonstrates strong performance on retrieval tasks in both languages, achieving reliable results across a variety of Korean- and English-language benchmarks.
|
| 15 |
+
This makes it well-suited for real-world applications that require high-quality semantic search in Korean, English, or both.
|
| 16 |
|
| 17 |
## Model Details
|
| 18 |
|