Instructions to use nateraw/codecarbon-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/codecarbon-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nateraw/codecarbon-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nateraw/codecarbon-text-classification") model = AutoModelForSequenceClassification.from_pretrained("nateraw/codecarbon-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nateraw/codecarbon-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/nateraw/codecarbon-text-classification/resolve/refs%2Fpr%2F3/pytorch_model.bin
- Command line
-
hf download hf://nateraw/codecarbon-text-classification@refs/pr/3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nateraw/codecarbon-text-classification/resolve/refs%2Fpr%2F3/pytorch_model.bin
433 MB
- Xet hash:
- 918757099502113ec531f84e77ffce2e5b494ed4e5280a59703f63cbd83f3bad
- Size of remote file:
- 433 MB
- SHA256:
- b07196b57e36479dcdf19466bd28bd5f931cbdffdece833151065835208dc98d
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