Feature Extraction
Transformers
Safetensors
sentence-transformers
Vietnamese
viclip_ot
vietnamese
image-text-retrieval
clip
optimal-transport
retrieval
custom_code
Instructions to use minhnguyent546/ViCLIP-OT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minhnguyent546/ViCLIP-OT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="minhnguyent546/ViCLIP-OT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("minhnguyent546/ViCLIP-OT", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use minhnguyent546/ViCLIP-OT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minhnguyent546/ViCLIP-OT", 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
File size: 422 Bytes
c7faf67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"auto_map": {
"AutoImageProcessor": "processing_viclip_ot.ViCLIPOTImageProcessor",
"AutoProcessor": "processing_viclip_ot.ViCLIPOTProcessor"
},
"crop_size": 224,
"image_processor_type": "ViCLIPOTImageProcessor",
"interpolation": "bicubic",
"mean": [
0.485,
0.456,
0.406
],
"processor_class": "ViCLIPOTProcessor",
"resize_size": 256,
"std": [
0.229,
0.224,
0.225
]
}
|