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 training_args.bin from nateraw/codecarbon-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/nateraw/codecarbon-text-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://nateraw/codecarbon-text-classification/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/nateraw/codecarbon-text-classification/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 6fe14ea6f3c5a559800fc54b70b010a495a8f6063836e8c6866b09e12fac5bf4
- Size of remote file:
- 3.06 kB
- SHA256:
- 9d985f700d6d0cc62ac3402fbd6af03508f12521a5a6ef16dd42ef9c21ca6dd5
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