Instructions to use remzicam/privacy_intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remzicam/privacy_intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="remzicam/privacy_intent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("remzicam/privacy_intent") model = AutoModelForSequenceClassification.from_pretrained("remzicam/privacy_intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# privacy_intent model for intent classification of privacy policies This model fine-tuned [privbert](https://huggingface.co/mukund/privbert) model on [PolicyIE dataset](https://github.com/wasiahmad/PolicyIE/blob/main/data/sanitized_split.zip). - Reference Paper: [Intent Classification and Slot Filling for Privacy Policies](https://aclanthology.org/2021.acl-long.340/). <b>Labels (5 Intents)</b>: *(1) Data Collection/Usage: What, why and how user information is collected;* *(2) Data Sharing/Disclosure: What, why and how user information is shared with or collected by third parties;* *(3) Data Storage/Retention: How long and where user information will be stored;* *(4) Data Security/Protection: Protection measures for user information;* *(5) Other: Other privacy practices that do not fall into the above four categories.* # Usage With text generation pipeline ```python from transformers import pipeline pipe = pipeline('text-classification', "remzicam/privacy_intent") text = "…b7a7f3a -
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