Instructions to use codeparrot/codeparrot-small-complexity-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codeparrot/codeparrot-small-complexity-prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="codeparrot/codeparrot-small-complexity-prediction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-complexity-prediction") model = AutoModelForSequenceClassification.from_pretrained("codeparrot/codeparrot-small-complexity-prediction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6e92d26a2899754b30720a0b6a43c3b339001eef1b2d5584ea0bb72a1025ecb
- Size of remote file:
- 457 MB
- SHA256:
- 3aa223f991aa512f042629e32db9416e22ed8e16d1e82219ce56a63cc5c49fcd
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