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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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" | |
| } | |