Instructions to use apps1/draft_overall_student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apps1/draft_overall_student with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="apps1/draft_overall_student", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("apps1/draft_overall_student", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ea22a2cf14630d446758285709de634ff5f2e3b99a39eedd03e4385d4905394
- Size of remote file:
- 3.88 MB
- SHA256:
- aed882a7814dbca1e4cc587e67ba5116dd5fa019ccdeffe7d3d7e87276f51942
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