Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use gemasphi/laprador-query-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gemasphi/laprador-query-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gemasphi/laprador-query-encoder") 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-query-encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gemasphi/laprador-query-encoder") model = AutoModel.from_pretrained("gemasphi/laprador-query-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gemasphi/laprador-query-encoder: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/gemasphi/laprador-query-encoder/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://gemasphi/laprador-query-encoder@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gemasphi/laprador-query-encoder/resolve/refs%2Fpr%2F1/pytorch_model.bin
436 MB
- Xet hash:
- 9a59a0818dfe66440b9005f87e7cec28ec3e533a48d3a1515850b84325b5a044
- Size of remote file:
- 436 MB
- SHA256:
- dae0f9b1cd7c2d6be8ae4c17cfe40665097ffd2ca820d5360bcf98ab9902136d
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