Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
mteb
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use aspire/acge_text_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aspire/acge_text_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aspire/acge_text_embedding") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#9)
Browse files- Adding `safetensors` variant of this model (9f8df2cf45acba89edf13cf86521916efcc0d42c)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- 2_Dense/model.safetensors +3 -0
- model.safetensors +3 -0
2_Dense/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6c49c1fc6dc5bf2d42b8c16d9f787de90dba5e57a705c67b7d67d547f895c905
|
| 3 |
+
size 3673800
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c6ec3d802bff018c0ee48be067f60aae32a1d444d214663ee6888f53b6317b09
|
| 3 |
+
size 652146704
|