Sentence Similarity
sentence-transformers
PyTorch
distilbert
feature-extraction
text-embeddings-inference
Instructions to use OysterQAQ/ACGVoc2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OysterQAQ/ACGVoc2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OysterQAQ/ACGVoc2vec") 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] - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- 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 sha256:41f54c127862ee569fca35a0a3a5799663b9985197ad5020a9671d365684af76
|
| 3 |
+
size 1575104
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:391023ad2c75b15593d44574be5208bf8319196041c589503fee1927e1d831fb
|
| 3 |
+
size 538947416
|