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:
- b7a2b7eca1e96f9cedc5f415c060c69d28a172deeac3657059e8f9b69faaf6a1
- Size of remote file:
- 9.48 MB
- SHA256:
- c6552f4298e0b58beda67fc9225817b842d007f790ef9578c3a4fb23b540acae
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