Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Data-Lab
/
multilingual-e5-large-instruct-embedder_distill-tg

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:10190
loss:DistillationTripletLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Data-Lab/multilingual-e5-large-instruct-embedder_distill-tg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Data-Lab/multilingual-e5-large-instruct-embedder_distill-tg with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Data-Lab/multilingual-e5-large-instruct-embedder_distill-tg")
    
    sentences = [
        "острая",
        "Instruct: Найти похожие продукты на основе деталей\nQuery: Смесь специй для мяса Золото Индии, 30 гр None, специи, масала, мясные блюда, кулинария, пряности, None",
        "Instruct: Найти похожие продукты на основе деталей\nQuery: Кыстыбый с картофелем и грибами, 2 шт сырое тесто, картофель, грибы, шампиньоны, татарская кухня, выпечка, лук, чеснок, перец, сливочное масло, обжаривание, чай, закуска",
        "Instruct: Найти похожие продукты на основе деталей\nQuery: Горчица \"Дижонская\" пикантная приправа, французская кухня, сладковато-острый вкус, салатные заправки, специи и пряности, натуральный продукт, традиционный рецепт, закуски"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
multilingual-e5-large-instruct-embedder_distill-tg
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
KobanBanan's picture
KobanBanan
Add new SentenceTransformer model
e6176b2 verified over 1 year ago
  • 1_Pooling
    Add new SentenceTransformer model over 1 year ago
  • .gitattributes
    1.57 kB
    Add new SentenceTransformer model over 1 year ago
  • README.md
    363 kB
    Add new SentenceTransformer model over 1 year ago
  • config.json
    725 Bytes
    Add new SentenceTransformer model over 1 year ago
  • config_sentence_transformers.json
    201 Bytes
    Add new SentenceTransformer model over 1 year ago
  • model.safetensors
    2.24 GB
    xet
    Add new SentenceTransformer model over 1 year ago
  • modules.json
    349 Bytes
    Add new SentenceTransformer model over 1 year ago
  • sentence_bert_config.json
    53 Bytes
    Add new SentenceTransformer model over 1 year ago
  • sentencepiece.bpe.model
    5.07 MB
    xet
    Add new SentenceTransformer model over 1 year ago
  • special_tokens_map.json
    964 Bytes
    Add new SentenceTransformer model over 1 year ago
  • tokenizer.json
    17.1 MB
    xet
    Add new SentenceTransformer model over 1 year ago
  • tokenizer_config.json
    1.18 kB
    Add new SentenceTransformer model over 1 year ago