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 emissions.csv from nateraw/codecarbon-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 305 Bytes
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https://huggingface.co/nateraw/codecarbon-text-classification/resolve/refs%2Fpr%2F4/emissions.csv
- Command line
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hf download hf://nateraw/codecarbon-text-classification@refs/pr/4/emissions.csv
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curl -L -o emissions.csv https://huggingface.co/nateraw/codecarbon-text-classification/resolve/refs%2Fpr%2F4/emissions.csv
305 Bytes
| timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region | |
| 2022-02-07T20:20:47,8235a4c0-26d4-4141-9c9d-375503d02716,codecarbon,398.7036349773407,0.001664903606019315,0.011999320519491527,United States,USA,oregon,N,, | |