Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use gemasphi/laprador_untrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gemasphi/laprador_untrained with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gemasphi/laprador_untrained") 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] - Transformers
How to use gemasphi/laprador_untrained with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gemasphi/laprador_untrained") model = AutoModel.from_pretrained("gemasphi/laprador_untrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gemasphi/laprador_untrained: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/gemasphi/laprador_untrained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gemasphi/laprador_untrained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gemasphi/laprador_untrained/resolve/main/pytorch_model.bin
261 MB
- Xet hash:
- 85aecf830c631ad42a6da5a188e65ba368d3319c4b5f63cec509b49542f087b3
- Size of remote file:
- 261 MB
- SHA256:
- 06eab5324e4252e6f2936bd073e9962b1406adbad1d50a2356b0bed8406c8fb5
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