Feature Extraction
Transformers
Safetensors
leaf
food
environment
NLP
Eco-Score
products
multilingual
BERT
classification
Open Food Facts
climate
custom_code
Instructions to use baskra/leaf-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baskra/leaf-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="baskra/leaf-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("baskra/leaf-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 349 Bytes
8e32295 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"architectures": [
"LeafModel"
],
"auto_map": {
"AutoConfig": "configuration_leaf.LeafConfig",
"AutoModel": "modeling_leaf.LeafModel"
},
"model_name": "sentence-transformers/distiluse-base-multilingual-cased-v2",
"model_type": "leaf",
"num_classes": 2097,
"torch_dtype": "float32",
"transformers_version": "4.39.3"
}
|