Instructions to use marcosremar2/cefr-classifier-pt-c2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcosremar2/cefr-classifier-pt-c2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcosremar2/cefr-classifier-pt-c2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcosremar2/cefr-classifier-pt-c2") model = AutoModelForSequenceClassification.from_pretrained("marcosremar2/cefr-classifier-pt-c2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from marcosremar2/cefr-classifier-pt-c2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/marcosremar2/cefr-classifier-pt-c2/resolve/main/tokenizer.json
- Command line
-
hf download hf://marcosremar2/cefr-classifier-pt-c2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/marcosremar2/cefr-classifier-pt-c2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- cf6cba8fec8e562495eff6339ea09ef9a86229925fcae631a2821ad9c54693c3
- Size of remote file:
- 17.1 MB
- SHA256:
- 3ffb37461c391f096759f4a9bbbc329da0f36952f88bab061fcf84940c022e98
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