Instructions to use NTCAL/TrainedOnGPU_local_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/TrainedOnGPU_local_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/TrainedOnGPU_local_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/TrainedOnGPU_local_3") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/TrainedOnGPU_local_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b54f3852fcb65362fded505de60f38811c62fbd5293faecbb09ea8e48f311b1
- Size of remote file:
- 498 MB
- SHA256:
- 4442a13b79497faf4ed5a601c250e06421624d6679a830fd1b088ab8d04a5c7f
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