Instructions to use Heng666/codecarbon-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heng666/codecarbon-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Heng666/codecarbon-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Heng666/codecarbon-text-classification") model = AutoModelForSequenceClassification.from_pretrained("Heng666/codecarbon-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f25c8ba6f24b5c3cc9781fbf61293db387beaf8fbf29e9abfe0068d8d5f019a
- Size of remote file:
- 433 MB
- SHA256:
- 97c3f2d4363ecfaaef7cd9e5a0c67d0f125b6ff907722d113308caef89c2a577
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