Instructions to use privacy-tech-lab/LatBaseModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/LatBaseModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/LatBaseModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/LatBaseModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/LatBaseModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| widget: | |
| - text: "'boa\\\\\",\\\\\"postal_code\\\\\":\\\\\"1800-255\\\\\",\\\\\"address_1\\\\\":\\\\\"29 Avenida Marechal Gomes da Costa\\\\\",\\\\\"address_2\\\\\":\\\\\"#B1\\\\\",\\\\\"latitude\\\\\":\\\\\"<TARGET_LAT>\\\\\",\\\\\"localized_multi_line_address_display\\\\\":[\\\\\"29 Avenida Marechal Gomes da Costa\\\\\",\\\\\"#B1\\\\\",\\\\\"1800-255 Lisboa\\\\\"],\\\\\"localized_'" | |
| example_title: "Lat True Positive Example" | |
| - text: "'null,0,[[\\\\\"0x470b94bf8b0c2a57:0x4e95622f0174b205\\\\\",\\\\\"Charles University - Rectorate, Ovocný trh 560/5, 116 36 Staré Město\\\\\",[<TARGET_LAT>9999,14.4235127],\\\\\"5662540060515676677\\\\\"],\\\\\"Charles University - Rectorate\\\\\",[\\\\\"Ovocný trh 560/5\\\\\",\\\\\"116 36 Staré Město\\\\\"],4.'" | |
| example_title: "Lat False Positive Example" | |