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 training/examples/web-request.json from beno-rohman/product-embedding-e5-base: direct link, hf CLI and curl.
- Browser
- Download file 283 Bytes
-
https://huggingface.co/beno-rohman/product-embedding-e5-base/resolve/main/training/examples/web-request.json
- Command line
-
hf download hf://beno-rohman/product-embedding-e5-base/training/examples/web-request.json
-
curl -L -o web-request.json https://huggingface.co/beno-rohman/product-embedding-e5-base/resolve/main/training/examples/web-request.json
283 Bytes
| { | |
| "products": [ | |
| "Samsung Galaxy S8 smartphone", | |
| "Samsung S8 Android phone", | |
| "Protective case for Samsung Galaxy S8", | |
| "ASUS laptop computer", | |
| "Nike running shoes", | |
| "Sepatu lari Nike" | |
| ], | |
| "model": "base", | |
| "method": "pca", | |
| "include_embeddings": false | |
| } | |