Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use jhgan/ko-sbert-multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jhgan/ko-sbert-multitask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jhgan/ko-sbert-multitask") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use jhgan/ko-sbert-multitask with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jhgan/ko-sbert-multitask") model = AutoModel.from_pretrained("jhgan/ko-sbert-multitask") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by joaogante - opened
- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4325a7b9a927a72073f0139fe9707bb117beee93970821169e656168e51d9616
|
| 3 |
+
size 442736616
|