Instructions to use zcahjl3/STORY_ERROR_CLASSIFICATION with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zcahjl3/STORY_ERROR_CLASSIFICATION with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zcahjl3/STORY_ERROR_CLASSIFICATION")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zcahjl3/STORY_ERROR_CLASSIFICATION") model = AutoModelForSequenceClassification.from_pretrained("zcahjl3/STORY_ERROR_CLASSIFICATION", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bbc140c21d9794687232a4839ce6e5d749cf423324fe3d50c85038c7f4929a9
- Size of remote file:
- 3.58 kB
- SHA256:
- b10db560858175efd16bba461c10b6a31865285cde87aced91576dfb6525c56a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.