Token Classification
Transformers
ONNX
xlm-roberta
pii
privacy
redaction
accessibility-tree
ocr
computer-use
agentic
screen-capture
screenpipe
Instructions to use screenpipe/pii-redactor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use screenpipe/pii-redactor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="screenpipe/pii-redactor")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("screenpipe/pii-redactor") model = AutoModelForTokenClassification.from_pretrained("screenpipe/pii-redactor") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "XLMRobertaForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-private_person", | |
| "2": "I-private_person", | |
| "3": "B-private_email", | |
| "4": "I-private_email", | |
| "5": "B-private_phone", | |
| "6": "I-private_phone", | |
| "7": "B-private_address", | |
| "8": "I-private_address", | |
| "9": "B-private_url", | |
| "10": "I-private_url", | |
| "11": "B-private_id", | |
| "12": "I-private_id", | |
| "13": "B-private_date", | |
| "14": "I-private_date", | |
| "15": "B-private_company", | |
| "16": "I-private_company", | |
| "17": "B-private_handle", | |
| "18": "I-private_handle", | |
| "19": "B-private_channel", | |
| "20": "I-private_channel", | |
| "21": "B-private_repo", | |
| "22": "I-private_repo", | |
| "23": "B-secret", | |
| "24": "I-secret", | |
| "25": "B-private_sensitive", | |
| "26": "I-private_sensitive" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-private_address": 7, | |
| "B-private_channel": 19, | |
| "B-private_company": 15, | |
| "B-private_date": 13, | |
| "B-private_email": 3, | |
| "B-private_handle": 17, | |
| "B-private_id": 11, | |
| "B-private_person": 1, | |
| "B-private_phone": 5, | |
| "B-private_repo": 21, | |
| "B-private_sensitive": 25, | |
| "B-private_url": 9, | |
| "B-secret": 23, | |
| "I-private_address": 8, | |
| "I-private_channel": 20, | |
| "I-private_company": 16, | |
| "I-private_date": 14, | |
| "I-private_email": 4, | |
| "I-private_handle": 18, | |
| "I-private_id": 12, | |
| "I-private_person": 2, | |
| "I-private_phone": 6, | |
| "I-private_repo": 22, | |
| "I-private_sensitive": 26, | |
| "I-private_url": 10, | |
| "I-secret": 24, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |