Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
generated
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/st-v3-test-mpnet-base-allnli-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/st-v3-test-mpnet-base-allnli-stsb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/st-v3-test-mpnet-base-allnli-stsb") sentences = [ "Really? No kidding! ", "yeah really no kidding", "At the end of the fourth century was when baked goods flourished.", "The campaigns seem to reach a new pool of contributors." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from tomaarsen/st-v3-test-mpnet-base-allnli-stsb: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/tomaarsen/st-v3-test-mpnet-base-allnli-stsb/resolve/main/modules.json
- Command line
-
hf download hf://tomaarsen/st-v3-test-mpnet-base-allnli-stsb/modules.json
-
curl -L -o modules.json https://huggingface.co/tomaarsen/st-v3-test-mpnet-base-allnli-stsb/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |