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
| [ | |
| { | |
| "idx": 0, | |
| "name": "transformer", | |
| "path": "", | |
| "type": "custom_st.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "normalizer", | |
| "path": "1_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |