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
- Xet hash:
- 4e91d885fda80270fed8cbf496e0075b631168689408ce4013a01708137a7aa3
- Size of remote file:
- 545 MB
- SHA256:
- 96c22f12d5eb2e765ec33a881aa53861cb35689cc43120810250bb7e3f61c91d
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