Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
roberta
feature-extraction
text-embeddings-inference
Instructions to use charlesdedampierre/bunka-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use charlesdedampierre/bunka-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("charlesdedampierre/bunka-embedding") 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] - Transformers
How to use charlesdedampierre/bunka-embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("charlesdedampierre/bunka-embedding") model = AutoModel.from_pretrained("charlesdedampierre/bunka-embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a2405b9606378dd0a62baa0dfb2fdf5af5201a2f82ead8057d3ac6e773ad692
- Size of remote file:
- 329 MB
- SHA256:
- c7e8205c56c53bd60de1b3aff8ab8cb594c0b614736759ba25afb248bc8201b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.