Instructions to use openai/privacy-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/privacy-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="openai/privacy-filter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("openai/privacy-filter") model = AutoModelForTokenClassification.from_pretrained("openai/privacy-filter", device_map="auto") - Transformers.js
How to use openai/privacy-filter with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'openai/privacy-filter'); - Inference
- Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
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license: apache-2.0
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pipeline_tag: token-classification
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library_name: transformers
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# OpenAI Privacy Filter
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OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable.
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license: apache-2.0
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pipeline_tag: token-classification
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library_name: transformers
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tags:
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- transformers.js
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Pq234
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# OpenAI Privacy Filter
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OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable.
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