Text Classification
Transformers
Safetensors
English
code
code-classification
programming-language
codebert
xgboost
random-forest
gradient-boosting
ensemble
machine-learning
Instructions to use yashodhajayasinghe/nexar-quantum-language-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashodhajayasinghe/nexar-quantum-language-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yashodhajayasinghe/nexar-quantum-language-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yashodhajayasinghe/nexar-quantum-language-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06460488e2a9b8ee77f9d82b4524c13eea03c88cba2f5bdd32ef526daa67b3a5
- Size of remote file:
- 499 MB
- SHA256:
- a82772f054a1cae6e80207223f6304bb015fe0be8179b4c0ae788e616e08ef6d
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