Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
sentence-transformers
English
jina_clip
sentence-similarity
mteb
clip
vision
custom_code
🇪🇺 Region: EU
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
Quantized models having trouble with many input text tokens
#31 opened about 1 year ago
by
kiwigs
Image Embedding without URL
#29 opened over 1 year ago
by
Heidi0039
Can I fine-tune this model?
#28 opened over 1 year ago
by
webliupeng
How to create embeddings in the browser?
3
#16 opened almost 2 years ago
by
gnoel-ddh
Apple silicon MLX framework input/guidance
👍 1
1
#7 opened almost 2 years ago
by
paulmaksimovich