Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use NeuML/celeberty-small-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/celeberty-small-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/celeberty-small-embeddings") 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 NeuML/celeberty-small-embeddings with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NeuML/celeberty-small-embeddings") model = AutoModel.from_pretrained("NeuML/celeberty-small-embeddings", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from NeuML/celeberty-small-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/NeuML/celeberty-small-embeddings/resolve/main/model.safetensors
- Command line
-
hf download hf://NeuML/celeberty-small-embeddings/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/NeuML/celeberty-small-embeddings/resolve/main/model.safetensors
90.9 MB
- Xet hash:
- 6d447f798943050b782dcf66a88d248a926840de8bf416ba2422975a14f77aee
- Size of remote file:
- 90.9 MB
- SHA256:
- 5c585beb23b400ab6e9c586d10a2bc6ad26d64f3ad20b33671e49cc417fd04e8
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