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") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - 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
Download tokenizer.json from krumeto/text-class-tutorial-model2vec: direct link, hf CLI and curl.
- Browser
- Download file 987 kB
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https://huggingface.co/krumeto/text-class-tutorial-model2vec/resolve/main/tokenizer.json
- Command line
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hf download hf://krumeto/text-class-tutorial-model2vec/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/krumeto/text-class-tutorial-model2vec/resolve/main/tokenizer.json
987 kB
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