Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
Transformers.js
jina_clip
xlm-roberta
eva02
clip
sentence-similarity
retrieval
multimodal
multi-modal
crossmodal
cross-modal
mteb
clip-benchmark
vidore
custom_code
Instructions to use Qsevent77/jina-clip-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qsevent77/jina-clip-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Qsevent77/jina-clip-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Qsevent77/jina-clip-v2", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Qsevent77/jina-clip-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qsevent77/jina-clip-v2", 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] - Transformers.js
How to use Qsevent77/jina-clip-v2 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'Qsevent77/jina-clip-v2'); - Notebooks
- Google Colab
- Kaggle
File size: 584 Bytes
306867d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"auto_map": {
"AutoImageProcessor": "jinaai/jina-clip-implementation--processing_clip.JinaCLIPImageProcessor",
"AutoProcessor": "jinaai/jina-clip-implementation--processing_clip.JinaCLIPProcessor"
},
"fill_color": 0,
"image_processor_type": "JinaCLIPImageProcessor",
"interpolation": "bicubic",
"mean": [
0.48145466,
0.4578275,
0.40821073
],
"processor_class": "JinaCLIPProcessor",
"resize_mode": "shortest",
"size": 512,
"std": [
0.26862954,
0.26130258,
0.27577711
]
}
|