Instructions to use privacy-tech-lab/LngBaseModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/LngBaseModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/LngBaseModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/LngBaseModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/LngBaseModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| widget: | |
| - text: "'a\\\\\",\\\\\"zipcode\\\\\":\\\\\"130 00\\\\\",\\\\\"timezone\\\\\":\\\\\"Europe/Prague\\\\\",\\\\\"latitude\\\\\":\\\\\"50.08040\\\\\",\\\\\"longitude\\\\\":\\\\\"<TARGET_LNG>\\\\\",\\\\\"city\\\\\":\\\\\"Prague\\\\\",\\\\\"continent\\\\\":\\\\\"EU\\\\\"}\",\"czprg_37\":\"!function(){var e={64515:function(e,t,n){\\\\'" | |
| example_title: "Lng True Positive Example" | |
| - text: "'Al Moalla\\\\\"},{\\\\\"centroid\\\\\":\\\\\"POINT(55.591619 25.50406)\\\\\",\\\\\"geometry\\\\\":\\\\\"POLYGON((<TARGET_LNG> 25.447356,55.54679 25.541579,55.723851 25.541579,55.723851 25.447356,55.54679 25.447356))\\\\\",\\\\\"id\\\\\":\\\\\"70030076164683501'" | |
| example_title: "Lng False Positive Example" | |