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

jinaai
/
jina-clip-v1

Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
sentence-transformers
English
jina_clip
sentence-similarity
mteb
clip
vision
custom_code
🇪🇺 Region: EU
Model card Files Files and versions
xet
Community
32

Instructions to use jinaai/jina-clip-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use jinaai/jina-clip-v1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="jinaai/jina-clip-v1", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("jinaai/jina-clip-v1", trust_remote_code=True, dtype="auto")
  • Transformers.js

    How to use jinaai/jina-clip-v1 with Transformers.js:

    // npm i @huggingface/transformers
    import { pipeline } from '@huggingface/transformers';
    
    // Allocate pipeline
    const pipe = await pipeline('feature-extraction', 'jinaai/jina-clip-v1');
  • sentence-transformers

    How to use jinaai/jina-clip-v1 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("jinaai/jina-clip-v1", trust_remote_code=True)
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
jina-clip-v1
4.23 GB
Ctrl+K
Ctrl+K
  • 9 contributors
History: 48 commits
michael-guenther's picture
michael-guenther
Integrate with Sentence Transformers v5.4 (#32)
ceb3e44 about 2 months ago
  • onnx
    fix-onnx-weights (#23) almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    10 kB
    Integrate with Sentence Transformers v5.4 (#32) about 2 months ago
  • config.json
    1.93 kB
    feat-remove-unnecessary-code (#4) about 2 years ago
  • config_sentence_transformers.json
    205 Bytes
    Update config_sentence_transformers.json over 1 year ago
  • custom_st.py
    7.81 kB
    Integrate with Sentence Transformers v5.4 (#32) about 2 months ago
  • model.safetensors
    891 MB
    xet
    feat: add model about 2 years ago
  • modules.json
    246 Bytes
    feat: add sbert support (#25) over 1 year ago
  • preprocessor_config.json
    527 Bytes
    fix: processor class in preprocessor_config.json almost 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    891 MB
    xet
    feat: add model about 2 years ago
  • special_tokens_map.json
    695 Bytes
    feat: initial commit about 2 years ago
  • tokenizer.json
    712 kB
    feat: initial commit about 2 years ago
  • tokenizer_config.json
    1.24 kB
    feat: initial commit about 2 years ago
  • vocab.txt
    232 kB
    feat: initial commit about 2 years ago