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:
- 40ab0eb38e6b70b69653c455930a05049606216b12b5ecc78826be54abbe589b
- Size of remote file:
- 1.42 GB
- SHA256:
- d555f4ea55c005dee4bc81014a3e8e52679c3ec59085a7f4aa44dcdf5ec08fda
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