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:
- 00a0311e8328dd8a5f0cf572008583312a60607a1849326247f17609315f1660
- Size of remote file:
- 3.39 kB
- SHA256:
- b133baeeefd376da7b64ff45b4fdf0ee8c9024c2714531597a5fcfb8ffaf03c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.