Instructions to use NTCAL/TrainedOnGPU_03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/TrainedOnGPU_03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/TrainedOnGPU_03")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/TrainedOnGPU_03") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/TrainedOnGPU_03", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d146f8d79ccba99fb33e4f7446d6d227e58118debd432ab84c69663368d1331
- Size of remote file:
- 3.52 kB
- SHA256:
- a9c751c795b81e33881b79754c610cbdb5c5a45463fd928d0c2c38d298b38228
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