Text Classification
Transformers
TensorBoard
Safetensors
sentence-transformers
French
xlm-roberta
Generated from Trainer
feature-extraction
legal
taxation
fiscalité
tax
text-embeddings-inference
Instructions to use louisbrulenaudet/lemone-router-l with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use louisbrulenaudet/lemone-router-l with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="louisbrulenaudet/lemone-router-l")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("louisbrulenaudet/lemone-router-l") model = AutoModelForSequenceClassification.from_pretrained("louisbrulenaudet/lemone-router-l") - sentence-transformers
How to use louisbrulenaudet/lemone-router-l with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("louisbrulenaudet/lemone-router-l") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a2560aa96c1b2593d0052a8392a8743080353d15c33c928cb684823edd9bde1
- Size of remote file:
- 5.24 kB
- SHA256:
- 3d3c2434baba0f5a0f2ddbdfcc5404aef5e057ac292f3018686a0b9afc138e75
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