Instructions to use pax-k/privacy-filter-mini2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pax-k/privacy-filter-mini2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pax-k/privacy-filter-mini2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pax-k/privacy-filter-mini2") model = AutoModelForTokenClassification.from_pretrained("pax-k/privacy-filter-mini2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "answerdotai/ModernBERT-base", | |
| "_num_labels": 16, | |
| "architectures": [ | |
| "ModernBertForTokenClassification" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 50281, | |
| "classifier_activation": "gelu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "mean", | |
| "cls_token_id": 50281, | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "embedding_dropout": 0.0, | |
| "eos_token_id": 50282, | |
| "global_attn_every_n_layers": 3, | |
| "global_rope_theta": 160000.0, | |
| "gradient_checkpointing": false, | |
| "hidden_activation": "gelu", | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-account_number", | |
| "2": "B-private_address", | |
| "3": "B-private_date", | |
| "4": "B-private_email", | |
| "5": "B-private_person", | |
| "6": "B-private_phone", | |
| "7": "B-private_url", | |
| "8": "B-secret", | |
| "9": "I-account_number", | |
| "10": "I-private_address", | |
| "11": "I-private_date", | |
| "12": "I-private_email", | |
| "13": "I-private_person", | |
| "14": "I-private_phone", | |
| "15": "I-secret" | |
| }, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "label2id": { | |
| "B-account_number": 1, | |
| "B-private_address": 2, | |
| "B-private_date": 3, | |
| "B-private_email": 4, | |
| "B-private_person": 5, | |
| "B-private_phone": 6, | |
| "B-private_url": 7, | |
| "B-secret": 8, | |
| "I-account_number": 9, | |
| "I-private_address": 10, | |
| "I-private_date": 11, | |
| "I-private_email": 12, | |
| "I-private_person": 13, | |
| "I-private_phone": 14, | |
| "I-secret": 15, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "local_attention": 128, | |
| "local_rope_theta": 10000.0, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "model_type": "modernbert", | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 22, | |
| "pad_token_id": 50283, | |
| "position_embedding_type": "absolute", | |
| "reference_compile": true, | |
| "repad_logits_with_grad": false, | |
| "sep_token_id": 50282, | |
| "sparse_pred_ignore_index": -100, | |
| "sparse_prediction": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.48.0", | |
| "vocab_size": 50368 | |
| } | |