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
metadata
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