Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

Azion
/
e-commerce-bert-base-multilingual-cased

Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use Azion/e-commerce-bert-base-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Azion/e-commerce-bert-base-multilingual-cased with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Azion/e-commerce-bert-base-multilingual-cased")
    
    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 Azion/e-commerce-bert-base-multilingual-cased with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("Azion/e-commerce-bert-base-multilingual-cased")
    model = AutoModel.from_pretrained("Azion/e-commerce-bert-base-multilingual-cased")
  • Notebooks
  • Google Colab
  • Kaggle
e-commerce-bert-base-multilingual-cased / 1_Pooling
190 Bytes
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
EZlee's picture
EZlee
Upload 11 files
73e5cec over 2 years ago
  • config.json
    190 Bytes
    Upload 11 files over 2 years ago