Sentence Similarity
sentence-transformers
PyTorch
Transformers
Korean
bert
feature-extraction
TAACO
text-embeddings-inference
Instructions to use KDHyun08/TAACO_STS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KDHyun08/TAACO_STS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KDHyun08/TAACO_STS") 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 KDHyun08/TAACO_STS with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KDHyun08/TAACO_STS") model = AutoModel.from_pretrained("KDHyun08/TAACO_STS") - Notebooks
- Google Colab
- Kaggle
Upload with huggingface_hub
Browse files- .gitignore +1 -0
- README.md +1 -1
.gitignore
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
README.md
|
README.md
CHANGED
|
@@ -93,7 +93,7 @@ for i, (score, idx) in enumerate(zip(top_results[0], top_results[1])):
|
|
| 93 |
|
| 94 |
## Evaluation Results
|
| 95 |
|
| 96 |
-
์
|
| 97 |
|
| 98 |
```
|
| 99 |
์
๋ ฅ ๋ฌธ์ฅ: ์์ผ์ ๋ง์ดํ์ฌ ์์นจ์ ์ค๋นํ๊ฒ ๋ค๊ณ ์ค์ 8์ 30๋ถ๋ถํฐ ์์์ ์ค๋นํ์๋ค
|
|
|
|
| 93 |
|
| 94 |
## Evaluation Results
|
| 95 |
|
| 96 |
+
์ Usage๋ฅผ ์คํํ๊ฒ ๋๋ฉด ์๋์ ๊ฐ์ ๊ฒฐ๊ณผ๊ฐ ๋์ถ๋ฉ๋๋ค. 1์ ๊ฐ๊น์ธ์๋ก ์ ์ฌํ ๋ฌธ์ฅ์
๋๋ค.
|
| 97 |
|
| 98 |
```
|
| 99 |
์
๋ ฅ ๋ฌธ์ฅ: ์์ผ์ ๋ง์ดํ์ฌ ์์นจ์ ์ค๋นํ๊ฒ ๋ค๊ณ ์ค์ 8์ 30๋ถ๋ถํฐ ์์์ ์ค๋นํ์๋ค
|