Instructions to use kimnt93/test_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kimnt93/test_cls with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kimnt93/test_cls") 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] - setfit
How to use kimnt93/test_cls with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("kimnt93/test_cls") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kimnt93/test_cls: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/kimnt93/test_cls/resolve/main/tokenizer.json
- Command line
-
hf download hf://kimnt93/test_cls/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kimnt93/test_cls/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- c6c3c0f0dd5efbe1561d438bcb3893ce1641359d03f3747f5b7b3afaf5f4beb2
- Size of remote file:
- 17.1 MB
- SHA256:
- c835b069d7b8cd02b400e6247b83bc1840ab12bb1628d5b2e03c8d728de75558
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.