Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
dense
recruitment
job-description
applai
text-embeddings-inference
Instructions to use Smutypi3/applai-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Smutypi3/applai-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Smutypi3/applai-sbert") 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
- Xet hash:
- b78482fb63b47b04b9e64dfe635b97787775ae598be3f24592e31e33955aed56
- Size of remote file:
- 438 MB
- SHA256:
- ffc7b9aadd3b17ef0e35e65684423b4d786b9169238c2ac7267e93de652bf789
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.