Instructions to use vgarg/my-dataset-identification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vgarg/my-dataset-identification-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vgarg/my-dataset-identification-model") 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 vgarg/my-dataset-identification-model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("vgarg/my-dataset-identification-model") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vgarg/my-dataset-identification-model: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/vgarg/my-dataset-identification-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vgarg/my-dataset-identification-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vgarg/my-dataset-identification-model/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 557e13d3b4116833a82c300c0fa7cd4f7a1cb7ce37fdae48538d8d13037c444a
- Size of remote file:
- 438 MB
- SHA256:
- 2f026dccaadd9360aaf538e700ba59f4f274f50ee7363762fca020067db5fd28
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