Instructions to use NTCAL/TrainedOnGPU_02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/TrainedOnGPU_02 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/TrainedOnGPU_02")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/TrainedOnGPU_02") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/TrainedOnGPU_02", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 391d92a501fc79b3bde53bc6e7efdc48435cd7bac6b34ffa36d150821eefa84c
- Size of remote file:
- 498 MB
- SHA256:
- 0f79bb9c509b3504324ee1775d5caca16016c013ce9545155257c0e1ce588eda
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