Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use gemasphi/laprador_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gemasphi/laprador_trained with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gemasphi/laprador_trained") 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_trained with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gemasphi/laprador_trained") model = AutoModel.from_pretrained("gemasphi/laprador_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gemasphi/laprador_trained: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/gemasphi/laprador_trained/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://gemasphi/laprador_trained@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gemasphi/laprador_trained/resolve/refs%2Fpr%2F1/pytorch_model.bin
261 MB
- Xet hash:
- 9752fe127a9ae319ff9551782b34f5f41c1cad9ee01bce1f9efc1a9b172e7390
- Size of remote file:
- 261 MB
- SHA256:
- d99f431b2429d67dee2c0f6a2f9846be56a710587d6609ca674db7728b107812
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