Instructions to use Abhibeats95/output_path with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhibeats95/output_path with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhibeats95/output_path")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhibeats95/output_path") model = AutoModelForSequenceClassification.from_pretrained("Abhibeats95/output_path") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e88d38e9406cab59a678b0e6d52887e5e4d1f5428491c0ec6ceab37157dae5a
- Size of remote file:
- 4.66 kB
- SHA256:
- 72c32cfa7116293071fd7a4c2338a41d3d3ef537316e52071bdacdfae08ac2a3
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