Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use lemon-mint/LLM-Router-Test-02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lemon-mint/LLM-Router-Test-02 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lemon-mint/LLM-Router-Test-02")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lemon-mint/LLM-Router-Test-02") model = AutoModelForSequenceClassification.from_pretrained("lemon-mint/LLM-Router-Test-02") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19c239e09312b43d03e40bc0854d2d13bd68afa97c54196d5c9fcad5138ae72a
- Size of remote file:
- 268 MB
- SHA256:
- 3e7ba925d8f211a6e05cadf10bb865b37dc59ca6fc6e37d8961d76ffc50bb444
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