Sentence Similarity
sentence-transformers
PyTorch
roberta
feature-extraction
text-embeddings-inference
Instructions to use seanfarrell/set_fit_experiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seanfarrell/set_fit_experiment with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seanfarrell/set_fit_experiment") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from seanfarrell/set_fit_experiment: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/seanfarrell/set_fit_experiment/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://seanfarrell/set_fit_experiment/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/seanfarrell/set_fit_experiment/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- c4151240765de43789a5328a2d8cc99c4977c7e02315a546a5798d5a7397fb9a
- Size of remote file:
- 1.42 GB
- SHA256:
- 8ad5c362147033eb43a3236346b619987aa60adea5bd3f76b040b2da6ab2ec4d
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