Instructions to use bicmol/NLBSE-Python-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bicmol/NLBSE-Python-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bicmol/NLBSE-Python-final")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bicmol/NLBSE-Python-final") model = AutoModelForSequenceClassification.from_pretrained("bicmol/NLBSE-Python-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 607c62a4ec7288523475221aab61774a408b496e8855d490ef226e74df811f8a
- Size of remote file:
- 1.42 GB
- SHA256:
- 83ba667cc22a4d33d3d513cbac3026cf41c8b6eddc0884922b6c77ff8dfd9b21
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