Instructions to use NTCAL/TrainedOnGPU_local_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/TrainedOnGPU_local_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/TrainedOnGPU_local_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/TrainedOnGPU_local_2") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/TrainedOnGPU_local_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fb4ccacac095b9d884c6f29a1190c07c5e76cf0b53ed3be5a33dea6484d3acd
- Size of remote file:
- 498 MB
- SHA256:
- 0f2e1ebc4a11da6eb0d26db1379457d3bb96a130f5f9f7bcd980e066919e0410
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.