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:
- e0f6fe6791cf6ac5b34ed4fd733e0ed8041c864f4e4669c7dd72b2adc3de849d
- Size of remote file:
- 4.49 MB
- SHA256:
- a06c42a72a20bf102430d594ae951628d516a3b5a1d1bc017f6a32be7f3eac9d
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