Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
product-similarity
product-embeddings
weak-supervision
text-embeddings-inference
Instructions to use beno-rohman/product-embedding-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use beno-rohman/product-embedding-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("beno-rohman/product-embedding-e5-base") 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 tokenizer.json from beno-rohman/product-embedding-e5-base: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/beno-rohman/product-embedding-e5-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://beno-rohman/product-embedding-e5-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/beno-rohman/product-embedding-e5-base/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 0e5e8194f58b9752ea875e6a5938c7d7ac928bd5912e0f9f98e18b5745d28265
- Size of remote file:
- 17.1 MB
- SHA256:
- 937f4a82d2465952cc36b8b530eff763b9bb84f82bbb0cacb81b8c1dc750e6d2
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