Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
text-embeddings-inference
Instructions to use dwulff/mpnet-personality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dwulff/mpnet-personality with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dwulff/mpnet-personality") 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -37,7 +37,7 @@ from sentence_transformers import SentenceTransformer
|
|
| 37 |
sentences = ["Rarely think about how I feel.", "Make decisions quickly."]
|
| 38 |
|
| 39 |
# load model
|
| 40 |
-
model = SentenceTransformer('
|
| 41 |
|
| 42 |
# extract embeddings
|
| 43 |
embeddings = model.encode(sentences)
|
|
|
|
| 37 |
sentences = ["Rarely think about how I feel.", "Make decisions quickly."]
|
| 38 |
|
| 39 |
# load model
|
| 40 |
+
model = SentenceTransformer('dwulff/mpnet-personality')
|
| 41 |
|
| 42 |
# extract embeddings
|
| 43 |
embeddings = model.encode(sentences)
|