Sentence Similarity
sentence-transformers
Safetensors
Transformers
Korean
deberta-v2
feature-extraction
text-embeddings-inference
Instructions to use bi-matrix/gmatrix-embedding1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bi-matrix/gmatrix-embedding1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bi-matrix/gmatrix-embedding1") 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] - Transformers
How to use bi-matrix/gmatrix-embedding1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bi-matrix/gmatrix-embedding1") model = AutoModel.from_pretrained("bi-matrix/gmatrix-embedding1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "
|
| 3 |
"architectures": [
|
| 4 |
"DebertaV2Model"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "kakaobank/kf-deberta-base",
|
| 3 |
"architectures": [
|
| 4 |
"DebertaV2Model"
|
| 5 |
],
|