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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from zcahjl3/STORY_ERROR_CLASSIFICATION: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/zcahjl3/STORY_ERROR_CLASSIFICATION/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zcahjl3/STORY_ERROR_CLASSIFICATION/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zcahjl3/STORY_ERROR_CLASSIFICATION/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- d8576602036643506308a5bafaadfd1236507ed7a198dcac2f62787031e35e3c
- Size of remote file:
- 268 MB
- SHA256:
- 0435cda4c3f2abb79ae55074ed39962d493e09337d86469512f76f5ad665369a
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