Instructions to use NTCAL/TrainedOnGPU_local_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/TrainedOnGPU_local_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/TrainedOnGPU_local_5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/TrainedOnGPU_local_5") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/TrainedOnGPU_local_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04047b81e356538403e7166d2068fb3307ad6965cd47765ffdb15720fd475509
- Size of remote file:
- 1.42 GB
- SHA256:
- 12c695e6bddec572d831380996a244f19b1073bbb4aab874f751bd3ca01b3a56
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