Instructions to use marcosremar2/cefr-classifier-pt-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcosremar2/cefr-classifier-pt-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcosremar2/cefr-classifier-pt-es")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcosremar2/cefr-classifier-pt-es") model = AutoModelForSequenceClassification.from_pretrained("marcosremar2/cefr-classifier-pt-es") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf6cba8fec8e562495eff6339ea09ef9a86229925fcae631a2821ad9c54693c3
- Size of remote file:
- 17.1 MB
- SHA256:
- 3ffb37461c391f096759f4a9bbbc329da0f36952f88bab061fcf84940c022e98
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