Instructions to use promforge/output_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use promforge/output_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="promforge/output_dir")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("promforge/output_dir") model = AutoModelForSequenceClassification.from_pretrained("promforge/output_dir") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80b2a22e725c82fc8a35846f9478e89064d5c4dccb5f1b78ede8de4790402e4a
- Size of remote file:
- 4.98 kB
- SHA256:
- 880d44e041aa1cf08f3c8c5565f8133cdfadcf51620c3745de755087823d1470
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