Instructions to use KalaiselvanD/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/output") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14f4d777cb2bb45cd5e62db28dc4f4b238067e485c73c0380d012ab72af053d4
- Size of remote file:
- 46.7 MB
- SHA256:
- 3e7b1c9159dd9720ba399be5d4cf271346639956ca0f9cdd98dc7bbc052f9629
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