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
| { | |
| "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 | |
| ] | |
| } | |