Instructions to use krumeto/text-class-tutorial-model2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use krumeto/text-class-tutorial-model2vec with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("krumeto/text-class-tutorial-model2vec") - sentence-transformers
How to use krumeto/text-class-tutorial-model2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("krumeto/text-class-tutorial-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:
- b46eb67f3f87df06f86b08478b802f135f34d5b90eea8bf6c763e4391b147f4a
- Size of remote file:
- 7.5 MB
- SHA256:
- 20e15e0a0f2b1cee5f010de424ba0fdcea58826b64ad456d45ca683bf04b1a10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.