Sentence Similarity
sentence-transformers
Safetensors
English
mteb
Sentence Transformers
Eval Results (legacy)
Instructions to use cjell/text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cjell/text with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cjell/text") 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:
- 6fd3bbbded5aeb2e73d73f54f36b10edeece7e32480cdea2e9b253c5ae71d80a
- Size of remote file:
- 368 Bytes
- SHA256:
- e7989e94b5b809d895a9521b708312c1ccd333e183effebaf3838908da2acd53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.