Instructions to use hschang98/codecarbon-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hschang98/codecarbon-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hschang98/codecarbon-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hschang98/codecarbon-text-classification") model = AutoModelForSequenceClassification.from_pretrained("hschang98/codecarbon-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d22eb49255004f14d4b0af295f59c3c6370ac31c58d15305f6c68f4dac498a1d
- Size of remote file:
- 4.92 kB
- SHA256:
- d3b7fed359a036ed26c8b4ad6f1d00fc8f1e06f8e12010e638f8cce9ddd20c30
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