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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f17c4be74c6b55f491cc7c76ec7fb38ad8c41af633ae43c74a8a54d3bedd9eb
- Size of remote file:
- 4.92 kB
- SHA256:
- 32435f587b9bccbaa4827ee32dc9c4c181484d85a2294c811e9cfbc5670e1991
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