Instructions to use DhanasriArul/Model2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use DhanasriArul/Model2vec with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("DhanasriArul/Model2vec") - sentence-transformers
How to use DhanasriArul/Model2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DhanasriArul/Model2vec") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7abf407e9f8c5c70bc35de579582b5ba02a7ede091389676b21fe7305042e068
- Size of remote file:
- 36.5 MB
- SHA256:
- 1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.