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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1f3c5d99740f4f2c60a9d25052947c4135f0267eee3349e8011289102cdcd68
- Size of remote file:
- 433 MB
- SHA256:
- e59111a312afdf7bd941e566fc10d10d3f6fb88b8837ac2de4f6757765b0c579
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.