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
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_viclip_ot.ViCLIPOTProcessor" | |
| }, | |
| "instruction_mode": "auto", | |
| "processor_class": "ViCLIPOTProcessor", | |
| "qwen_instruction": "Retrieve images or text relevant to the user's query.", | |
| "text_model_name": "keepitreal/vietnamese-sbert" | |
| } | |