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
Add data attribution section for AI transparency
#30
by MikeDoes - opened
README.md
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@@ -4,6 +4,27 @@ 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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---
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# OpenAI Privacy Filter
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library_name: transformers
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tags:
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- transformers.js
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datasets:
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- ai4privacy/pii-masking-300k
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model-index:
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- name: privacy-filter
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results:
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- task:
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type: token-classification
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name: PII Detection and Masking
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dataset:
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type: ai4privacy/pii-masking-300k
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name: PII-Masking-300k
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metrics:
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- type: f1
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value: 0.96
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name: F1 Score
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- type: precision
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value: 0.9404
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name: Precision
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- type: recall
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value: 0.9804
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name: Recall
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---
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# OpenAI Privacy Filter
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