Sentence Similarity
sentence-transformers
Safetensors
Korean
English
roberta
feature-extraction
text-embeddings-inference
Instructions to use FronyAI/frony-embed-medium-ko-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FronyAI/frony-embed-medium-ko-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FronyAI/frony-embed-medium-ko-v2") 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
|
@@ -98,9 +98,9 @@ Then you can load this model and run inference.
|
|
| 98 |
from sentence_transformers import SentenceTransformer
|
| 99 |
|
| 100 |
# Download from the π€ Hub
|
| 101 |
-
model = SentenceTransformer("FronyAI/frony-embed-medium-ko-
|
| 102 |
-
# Run inference
|
| 103 |
|
|
|
|
| 104 |
# '<Q>' is special token for query.
|
| 105 |
queries = [
|
| 106 |
'<Q>μλ
νμΈμ',
|
|
@@ -112,6 +112,14 @@ passages = [
|
|
| 112 |
'<P>λ°κ°μ΅λλ€',
|
| 113 |
]
|
| 114 |
embeddings = model.encode(passages)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 115 |
```
|
| 116 |
|
| 117 |
## Contact
|
|
|
|
| 98 |
from sentence_transformers import SentenceTransformer
|
| 99 |
|
| 100 |
# Download from the π€ Hub
|
| 101 |
+
model = SentenceTransformer("FronyAI/frony-embed-medium-ko-v2")
|
|
|
|
| 102 |
|
| 103 |
+
# Run inference
|
| 104 |
# '<Q>' is special token for query.
|
| 105 |
queries = [
|
| 106 |
'<Q>μλ
νμΈμ',
|
|
|
|
| 112 |
'<P>λ°κ°μ΅λλ€',
|
| 113 |
]
|
| 114 |
embeddings = model.encode(passages)
|
| 115 |
+
|
| 116 |
+
# Matryoshka Embeddings (half of the original dimension)
|
| 117 |
+
# '<Q>' is special token for query.
|
| 118 |
+
queries = [
|
| 119 |
+
'<Q>μλ
νμΈμ',
|
| 120 |
+
]
|
| 121 |
+
embeddings = model.encode(queries, normalize_embeddings=False, convert_to_tensor=True)[:, :512]
|
| 122 |
+
embeddings = F.normalize(embeddings, p=2, dim=-1)
|
| 123 |
```
|
| 124 |
|
| 125 |
## Contact
|