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: 462 Bytes
8e32295 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | from typing import Literal
from transformers import PretrainedConfig
class LeafConfig(PretrainedConfig):
model_type = "leaf"
def __init__(
self,
num_classes: int = 2097,
model_name: str = Literal["BAAI/bge-m3", "sentence-transformers/distiluse-base-multilingual-cased-v2"],
**kwargs,
):
self.num_classes = num_classes
self.model_name = model_name
super().__init__(**kwargs)
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