Feature Extraction
Transformers
Safetensors
Chinese
tianmu_mere
multimodal
embedding
retrieval
e-commerce
product-understanding
image-text-retrieval
fashion
Instructions to use TianmuLab/Tianmu-MERE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TianmuLab/Tianmu-MERE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TianmuLab/Tianmu-MERE")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TianmuLab/Tianmu-MERE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor/preprocessor_config.json from TianmuLab/Tianmu-MERE: direct link, hf CLI and curl.
- Browser
- Download file 394 Bytes
-
https://huggingface.co/TianmuLab/Tianmu-MERE/resolve/main/processor/preprocessor_config.json
- Command line
-
hf download hf://TianmuLab/Tianmu-MERE/processor/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/TianmuLab/Tianmu-MERE/resolve/main/processor/preprocessor_config.json
394 Bytes
| { | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "SiglipProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| } | |
| } | |