Instructions to use privacy-tech-lab/CityDistilledModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/CityDistilledModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/CityDistilledModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/CityDistilledModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/CityDistilledModel") - Notebooks
- Google Colab
- Kaggle
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README.md
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---
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widget:
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- text: "'\"Europe\",\"continent_code\":\"EU\",\"country\":\"Portugal\",\"country_code\":\"PT\",\"state\":\"\",\"city\":\"\",\"postal\":\"\",\"time_zone\":\"Europe/<TARGET_CITY>\",\"region\":\"EMEA\",\"ipAddress\":\"94.46.24.35\",\"latitude\":38.7057,\"longitude\":-9.1359}'"
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example_title: "City True Positive Example"
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- text: "'ass=\"cover-image__popup-view__caption-wrapper text-white\"><p><b>ONLINE COVER</b> Boosting Immunity. The image shows a man in <TARGET_CITY> receiving a second dose of the Pfizer-BioNTech COVID-19 vaccine in late January 2021 during the launch of Israel’s SARS-CoV-'"
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example_title: "City False Positive Example"
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