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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Update dataset YAML metadata for model
This is a pull request to add a dataset, [`codeparrot/codecomplex`](https://huggingface.co/datasets/codeparrot/codecomplex), to the metadata for your model (defined in the `YAML` block of your model's `README.md`). The pull request was made by [librarian-bot](https://huggingface.co/librarian-bot) and used a combination of rules and/or machine learning to suggest this additional metadata. If this suggestion is incorrect, feel free to close this pull request. Librarian Bot was made by [@davanstrien](https://huggingface.co/davanstrien); feel free to get in touch with feedback.8cc4501
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