Instructions to use privacy-tech-lab/CityModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/CityModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/CityModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/CityModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/CityModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a10d7d3d286ca8d62b97e1adbee70e3a1d27ce61a13c4dae519d81113abc3a4
- Size of remote file:
- 3.12 kB
- SHA256:
- 09b834b8b41b7f13c6c69d728ad3d870ae4b9a38f6ca08713c10a55b001011eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.